The main Beluga namespace. More...

Namespaces
	actions

	clusterizer_detail

	detail

	io

	policies

	state_detail

	testing

	views

	weight_detail

Classes
class	Amcl
	Implementation of the Adaptive Monte Carlo Localization (AMCL) algorithm. More...

struct	AmclParams
	Struct containing parameters for the Adaptive Monte Carlo Localization (AMCL) implementation. More...

class	BaseDenseGrid2
	Dense 2D grid base type. More...

class	BaseLaserScan
	Laser scan 2D base type. More...

class	BaseLinearGrid2
	Linear 2D grid base type. More...

class	BaseOccupancyGrid2
	Occupancy 2D grid base type. More...

class	BaseRegularGrid
	Regularly spaced N dimensional grid base type. More...

struct	BeamModelParam
	Parameters used to construct a BeamSensorModel instance. More...

class	BeamSensorModel
	Beam sensor model for range finders. More...

struct	BearingModelParam
	Parameters used to construct a BearingSensorModel instance. More...

class	BearingSensorModel
	Generic bearing sensor model, for both 2D and 3D state types. More...

struct	BearingSensorModelTests

class	Bresenham2i
	Bresenham's 2D line drawing algorithm, optimized for integer arithmetic. More...

class	CircularArray
	An implementation of generic, non-threadsafe circular array. More...

struct	common_tuple_type
	Meta-function that computes a common tuple type given two tuple-like types T and U. More...

struct	common_tuple_type< T, U, std::index_sequence< I... > >
	`common_tuple_type` specialization for `std::index_sequence`. More...

struct	decay_tuple_like
	Meta-function that decays a tuple like type and its members. More...

struct	decay_tuple_like< TupleLike< Args... >, std::enable_if_t< is_tuple_like_v< std::decay_t< TupleLike< Args... > > > > >
	`decay_tuple_like` specialization for tuples. More...

class	DifferentialDriveModel
	Sampled odometry model for a differential drive. More...

struct	DifferentialDriveModelParam
	Parameters to construct a DifferentialDriveModel instance. More...

class	ExponentialFilter
	Callable type implementing an exponential filter. More...

struct	has_common_tuple_type
	Meta-function that checks for the existence of a common tuple type. More...

struct	has_common_tuple_type< T, U, std::void_t< common_tuple_type_t< T, U > > >
	`has_common_tuple_type` specialization for tuple-like types T and U for which a common tuple type exists. More...

struct	has_single_element
	Meta-function that returns true if there is a single element of type T in the tuple-like type. More...

struct	Hasher

class	IndexingIterator
	A random access iterator for any indexable container. More...

struct	is_tuple_like
	Meta-function that returns true if T is a tuple-like type. More...

struct	LandmarkBearingDetection
	Landmark bearing detection data. More...

class	LandmarkMap
	Basic 3D landmark map datatype. More...

struct	LandmarkModelParam
	Parameters used to construct a LandmarkSensorModel instance (both 2D and 3D). More...

struct	LandmarkPositionDetection
	Landmark bearing detection data. More...

class	LandmarkSensorModel
	Generic landmark model for discrete detection sensors (both 2D and 3D). More...

struct	Less

class	LikelihoodFieldModel
	Likelihood field sensor model for range finders. More...

struct	LikelihoodFieldModelParam
	Parameters used to construct a LikelihoodFieldModel instance. More...

struct	multivariate_distribution_traits
	Forward declaration of the multivariate_distribution_traits class template. More...

struct	multivariate_distribution_traits< T, std::enable_if_t< std::is_base_of_v< Eigen::EigenBase< T >, T > > >
	Specialization for types derived from `Eigen::EigenBase`. More...

struct	multivariate_distribution_traits< T, std::enable_if_t< std::is_base_of_v< Sophus::SE2Base< T >, T > > >
	Specialization for types derived from Sophus::SE2Base. More...

struct	multivariate_distribution_traits< T, std::enable_if_t< std::is_base_of_v< Sophus::SE3Base< T >, T > > >
	Specialization for types derived from Sophus::SE3Base. More...

struct	multivariate_distribution_traits< T, std::enable_if_t< std::is_base_of_v< Sophus::SO2Base< T >, T > > >
	Specialization for types derived from Sophus::SO2Base. More...

struct	multivariate_distribution_traits< T, std::enable_if_t< std::is_base_of_v< Sophus::SO3Base< T >, T > > >
	Specialization for types derived from Sophus::SO3Base. More...

class	MultivariateNormalDistribution
	Multivariate normal distribution. More...

class	MultivariateNormalDistributionParam
	Multivariate normal distribution parameter set class. More...

class	MultivariateUniformDistribution
	Primary template for a multivariate uniform distribution. More...

class	MultivariateUniformDistribution< Sophus::SE2d, Eigen::AlignedBox2d >
	Specialization of multivariate uniform distribution for bounding regions in 2D space. More...

class	MultivariateUniformDistribution< Sophus::SE2d, OccupancyGrid >
	Specialization of multivariate uniform distribution for occupancy grids. More...

class	MultivariateUniformDistribution< Sophus::SE3d, Eigen::AlignedBox3d >
	Specialization of multivariate uniform distribution for bounding regions in 3D space. More...

struct	NDTCell
	Representation for a cell of a N dimensional NDT cell. More...

struct	NDTModelParam
	Parameters used to construct a NDTSensorModel instance. More...

class	NDTSensorModel
	NDT sensor model for range finders. More...

class	Numeric
	Helper for creating strongly typed numeric types. More...

class	OmnidirectionalDriveModel
	Sampled odometry model for an omnidirectional drive. More...

struct	OmnidirectionalDriveModelParam
	Parameters to construct an OmnidirectionalDriveModel instance. More...

struct	particle_traits
	Common traits of all particle types. See Page requirements as well. More...

class	ParticleClusterizer
	Particle clusterizer implementation. More...

struct	ParticleClusterizerParam
	Parameters used to construct a ParticleClusterizer instance. More...

struct	policy
	Forward declaration of policy. More...

struct	policy_base
	Implementation detail for a policy base object. More...

class	Ray2d
	Castable 2D ray. More...

class	SparseGridTests

class	SparseValueGrid
	Generic N dimensional sparse value regular grid. More...

struct	spatial_hash
	Callable class, allowing to calculate the hash of a particle state. More...

class	spatial_hash< Sophus::SE2d, void >

struct	spatial_hash< State >

class	spatial_hash< std::array< T, N >, std::enable_if_t< std::is_arithmetic_v< T >, void > >
	Specialization for arrays. More...

class	spatial_hash< Tuple< Types... >, std::enable_if_t<(std::is_arithmetic_v< Types > &&...), void > >
	Specialization for tuples. More...

class	StationaryModel
	A stationary motion model. More...

class	ThrunRecoveryProbabilityEstimator
	Random particle probability estimator. More...

struct	tuple_index
	Meta-function that returns the tuple index of the element whose type is T. More...

class	TupleContainer
	Primary template for a tuple of containers. More...

class	TupleContainer< InternalContainer, std::tuple< Types... > >
	An implementation of a tuple of containers, with an interface that looks like a container of tuples. More...

class	TupleVector
	Shorthand for a tuple of vectors with the default allocator. More...

class	ValueGrid2
	Generic 2D linear value grid. More...

Typedefs
template<class... Args>
using	any_policy = policy< std::function< bool(Args...)> >
	Type erased policy. More...

template<typename Derived >
using	BaseRegularGrid2 = BaseRegularGrid< Derived, 2 >
	Convenience alias for a 2D base regular grid. More...

template<typename Derived >
using	BaseRegularGrid3 = BaseRegularGrid< Derived, 3 >
	Convenience alias for a 3D base regular grid. More...

template<class LandmarkMap >
using	BearingSensorModel2d = BearingSensorModel< LandmarkMap, Sophus::SE2d >
	Sensor model based on discrete landmarks bearing detection for 2D state types. More...

template<class LandmarkMap >
using	BearingSensorModel3d = BearingSensorModel< LandmarkMap, Sophus::SE3d >
	Sensor model based on discrete landmarks bearing detection for 3D state types. More...

using	BearingSensorModelTestsTypes = ::testing::Types< Sensor2D, Sensor3D >

using	Cluster = Numeric< std::size_t, struct ClusterTag >
	Cluster type, as a strongly typed `std::size_t`. More...

template<typename T , typename U >
using	common_tuple_type_t = typename common_tuple_type< T, U >::type
	Convenience template type alias for `common_tuple_type`. More...

template<class T >
using	decay_tuple_like_t = typename decay_tuple_like< T >::type
	Convenience template type alias for `decay_tuple_like`. More...

using	DifferentialDriveModel2d = DifferentialDriveModel< Sophus::SE2d >
	Alias for a 2D differential drive model, for convinience. More...

using	DifferentialDriveModel3d = DifferentialDriveModel< Sophus::SE3d >
	Alias for a 3D differential drive model, for convinience. More...

using	LandmarkBearing3 = Eigen::Vector3d
	Bearing of a landmark in the sensor reference frame. More...

using	LandmarkCategory = uint32_t
	Type used to represent landmark categories. More...

using	LandmarkMapBoundaries = Eigen::AlignedBox3d
	Boundaries of a landmark map. More...

using	LandmarkPosition3 = Eigen::Vector3d
	Position of a landmark in the world reference frame. More...

template<class LandmarkMap >
using	LandmarkSensorModel2d = LandmarkSensorModel< LandmarkMap, Sophus::SE2d >
	Sensor model based on discrete landmarks for 2D state types. More...

template<class LandmarkMap >
using	LandmarkSensorModel3d = LandmarkSensorModel< LandmarkMap, Sophus::SE3d >
	Sensor model based on discrete landmarks for 3D state types. More...

using	NDTCell2d = NDTCell< 2, double >
	Convenience alias for a 2D NDT cell with double representation. More...

using	NDTCell2f = NDTCell< 2, float >
	Convenience alias for a 2D NDT cell with float representation. More...

using	NDTCell3d = NDTCell< 3, double >
	Convenience alias for a 3D NDT cell with double representation. More...

using	NDTCell3f = NDTCell< 3, float >
	Convenience alias for a 3D NDT cell with float representation. More...

using	NDTModelParam2d = NDTModelParam< 2 >
	Convenience alias for a 2d parameters struct for the NDT sensor model. More...

using	NDTModelParam3d = NDTModelParam< 3 >
	Convenience alias for a 3d parameters struct for the NDT sensor model. More...

template<typename T , std::size_t N>
using	RollingWindow = CircularArray< T, N, CircularArrayFeatureFlags::kRolloverOnWrite\|CircularArrayFeatureFlags::kExtrapolateOnRead\|CircularArrayFeatureFlags::kLayoutReversal >
	Convenient type alias for a circular array that behaves like a rolling window. More...

using	Sensor2D = beluga::BearingSensorModel2d< LandmarkMap >

using	Sensor3D = beluga::BearingSensorModel3d< LandmarkMap >

using	SparseGridTestCases = testing::Types< std::unordered_map< Eigen::Vector2i, int, Hasher >, std::map< Eigen::Vector2i, int, Less > >

template<typename MapType >
using	SparseValueGrid2 = SparseValueGrid< MapType, 2 >
	Convenience alias for 2D sparse value grids. More...

template<typename MapType >
using	SparseValueGrid3 = SparseValueGrid< MapType, 3 >
	Convenience alias for 3D sparse value grids. More...

template<class T >
using	state_t = typename particle_traits< T >::state_type
	Type trait that returns the state type given a particle type. More...

using	strong_double = Numeric< double, struct StrongDoubleTag >

using	strong_size_t = Numeric< std::size_t, struct StrongSizeTTag >

using	strong_size_t_2 = Numeric< std::size_t, struct StrongSizeTTag2 >

template<class T , class TupleLike >
using	tuple_index_t = typename tuple_index< T, TupleLike >::type
	Convenience template type alias for `tuple_index`. More...

using	UUT = beluga::BeamSensorModel< StaticOccupancyGrid< 5, 5 > >

template<class T >
using	Vector = std::vector< T, std::allocator< T > >
	Shorthand for a vector with the default allocator. More...

using	Weight = Numeric< double, struct WeightTag >
	Weight type, as a strongly typed `double`. More...

template<class T >
using	weight_t = typename particle_traits< T >::weight_type
	Type trait that returns the weight type given a particle type. More...

Enumerations
enum	CircularArrayFeatureFlags : std::int8_t { CircularArrayFeatureFlags::kNone = 0x00, CircularArrayFeatureFlags::kRolloverOnWrite = 0x01, CircularArrayFeatureFlags::kExtrapolateOnRead = 0x02, CircularArrayFeatureFlags::kLayoutReversal = 0x04 }
	Feature flags for circular arrays. More...

Functions
template<class Range , class Scalar >
Sophus::Matrix2< Scalar >	calculate_covariance (Range &&range, const Sophus::Vector2< Scalar > &mean)

template<class Range , class WeightsRange , class Scalar >
Sophus::Matrix2< Scalar >	calculate_covariance (Range &&range, WeightsRange &&normalized_weights, const Sophus::Vector2< Scalar > &mean)
	Calculates the covariance of a range given its mean and the weights of each element. More...

template<class States , class Weights >
auto	cluster_based_estimate (States &&states, Weights &&weights, ParticleClusterizerParam parameters={})
	Computes a cluster-based estimate from a particle set. More...

template<class Range , std::enable_if_t<!is_particle_range_v< Range >, int > = 0>
auto	effective_sample_size (Range &&range)
	Calculate the ESS of a given a range of weights. More...

template<class T , class TupleLike >
constexpr decltype(auto)	element (TupleLike &&tuple) noexcept
	Returns element of a tuple like object whose type is T (or a possibly const reference to T). More...

template<class Poses , class Pose = ranges::range_value_t<Poses>, class Scalar = typename Pose::Scalar, typename = std::enable_if_t<std::is_same_v<Pose, typename Sophus::SE2<Scalar>>>>
std::pair< Sophus::SE2< Scalar >, Sophus::Matrix3< Scalar > >	estimate (Poses &&poses)
	Returns a pair consisting of the estimated mean pose and its covariance. More...

template<class Poses , class Weights , class Pose = ranges::range_value_t<Poses>, class Scalar = typename Pose::Scalar, typename = std::enable_if_t<std::is_same_v<Pose, typename Sophus::SE2<Scalar>>>>
std::pair< Sophus::SE2< Scalar >, Sophus::Matrix3< Scalar > >	estimate (Poses &&poses, Weights &&weights)
	Returns a pair consisting of the estimated mean pose and its covariance. More...

template<class Scalars , class Weights , class Scalar = ranges::range_value_t<Scalars>, typename = std::enable_if_t<std::is_arithmetic_v<Scalar>>>
std::pair< Scalar, Scalar >	estimate (Scalars &&scalars, Weights &&weights)
	Computes mean and variance of a range of weighted scalars. More...

template<class States , class Weights , class Clusters >
auto	estimate_clusters (States &&states, Weights &&weights, Clusters &&clusters)
	For each cluster, estimate the mean and covariance of the states that belong to it. More...

double	expected_aggregate_probability (std::vector< double > landmark_probs)

template<class T , class U >
constexpr auto &&	forward_like (U &&value) noexcept
	Returns a reference to a value which has similar properties to `T&&`. More...

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>
constexpr T &&	get (CircularArray< T, N, F > &&array) noexcept
	Gets an rvalue reference to the ith value in a given `array`. More...

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>
constexpr T &	get (CircularArray< T, N, F > &array) noexcept
	Gets an lvalue reference to the ith value in a given `array`. More...

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>
constexpr const T &&	get (const CircularArray< T, N, F > &&array) noexcept
	Gets a constant rvalue reference to the ith value in a given `array`. More...

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>
constexpr const T &	get (const CircularArray< T, N, F > &array) noexcept
	Gets a constant lvalue reference to the ith value in a given `array`. More...

BearingModelParam	get_default_model_params ()

template<typename T >
T	get_robot_pose_in_world ()

template<>
Sophus::SE2d	get_robot_pose_in_world< Sophus::SE2d > ()

template<>
Sophus::SE3d	get_robot_pose_in_world< Sophus::SE3d > ()

BeamModelParam	GetParams ()

auto	kld_condition (std::size_t min, double epsilon, double z=beluga::detail::kDefaultKldZ)
	Returns a callable object that verifies if the KLD condition is being satisfied. More...

auto	make_sensor_data (std::vector< std::tuple< double, double, double, uint32_t >> detections)

template<class T >
	MultivariateNormalDistribution (const T &, const typename multivariate_distribution_traits< T >::covariance_type &) -> MultivariateNormalDistribution< typename multivariate_distribution_traits< T >::result_type >
	Deduction guide to deduce the correct result type. More...

template<class Derived >
	MultivariateUniformDistribution (const BaseOccupancyGrid2< Derived > &) -> MultivariateUniformDistribution< Sophus::SE2d, Derived >
	Deduction guide for 2D occupancy grids. More...

	MultivariateUniformDistribution (const Eigen::AlignedBox2d &) -> MultivariateUniformDistribution< Sophus::SE2d, Eigen::AlignedBox2d >
	Deduction guide for bounding regions in SE2 space. More...

	MultivariateUniformDistribution (const Eigen::AlignedBox3d &) -> MultivariateUniformDistribution< Sophus::SE3d, Eigen::AlignedBox3d >
	Deduction guide for bounding regions in SE3 space. More...

template<class Range , class DistanceFunction , class NeighborsFunction >
auto	nearest_obstacle_distance_map (Range &&obstacle_map, DistanceFunction &&distance_function, NeighborsFunction &&neighbors_function)
	Returns a map where the value of each cell is the distance to the nearest obstacle. More...

constexpr bool	operator& (CircularArrayFeatureFlags mask, CircularArrayFeatureFlags flag)
	Bitwise AND operator overload to check of the presence of a feature `flag` in a feature `mask`. More...

template<typename T , std::size_t N, CircularArrayFeatureFlags F>
CircularArray< T, N, F > &	operator<< (CircularArray< T, N, F > &array, T value)
	Convenient stream operator overload to push a `value` to a circular `array`. More...

constexpr CircularArrayFeatureFlags	operator\| (CircularArrayFeatureFlags lflag, CircularArrayFeatureFlags rflag)
	Bitwise OR operator overload to combine two feature flags in a single mask-like flag. More...

template<typename T , std::size_t N, CircularArrayFeatureFlags F, CircularArrayFeatureFlags G>
constexpr void	swap (CircularArray< T, N, F > &a, CircularArray< T, N, G > &b)
	Swaps arrays `a` and `b`. More...

	TEST (BaselineRaycasting, Nominal)

	TEST (BeamSensorModel, GridUpdates)

	TEST (BeamSensorModel, ImportanceWeight)

	TEST (Bresenham, Modified)

	TEST (Bresenham, MultiPassGuarantee)

	TEST (Bresenham, Standard)

	TEST (Embed3DTests, Se2ToSe3)

	TEST (Embed3DTests, Se3ToSe2)

	TEST (NDTCellTests, Ostream)

	TEST (NDTCellTests, Product)

	TEST (NDTSensorModel2DTests, CanConstruct)

	TEST (NDTSensorModel2DTests, FitPoints)

	TEST (NDTSensorModel2DTests, Likelihoood)

	TEST (NDTSensorModel2DTests, LoadFromHDF5HappyPath)

	TEST (NDTSensorModel2DTests, LoadFromHDF5NonExistingFile)

	TEST (NDTSensorModel2DTests, MinLikelihood)

	TEST (NDTSensorModel2DTests, SensorModel)

	TEST (NDTSensorModel2DTests, ToCellsNotEnoughPointsInCell)

	TEST (NDTSensorModel3DTests, FitPoints)

	TEST (NDTSensorModel3DTests, LoadFromHDF5HappyPath)

	TEST (NDTSensorModel3DTests, LoadFromHDF5NonExistingFile)

	TEST (NDTSensorModel3DTests, SensorModel)

	TEST (NDTSensorModel3DTests, ToCellsNotEnoughPointsInCell)

	TEST (NumericTypeDef, hash)

	TEST (NumericTypeDef, implicitCasts)

	TEST (NumericTypeDef, numericLimits)

	TEST (Raycasting, Nominal)

	TEST (Raycasting, NonIdentityGridOrigin)

	TEST (TestAmclCore, InitializeFromPose)

	TEST (TestAmclCore, InitializeWithNoParticles)

	TEST (TestAmclCore, ParticlesDependentRandomStateGenerator)

	TEST (TestAmclCore, SelectiveResampleCanBeConstructed)

	TEST (TestAmclCore, TestRandomParticlesInserting)

	TEST (TestAmclCore, Update)

	TEST (TestAmclCore, UpdateWithNoParticles)

	TEST (TestAmclCore, UpdateWithParticles)

	TEST (TestAmclCore, UpdateWithParticlesForced)

	TEST (TestAmclCore, UpdateWithParticlesNoMotion)

Sophus::SE2d	To2d (const Sophus::SE3d &tf)
	Transforms a SE3 transform into a SE2 transform, by flattening the Z axis. More...

Sophus::SE3d	To3d (const Sophus::SE2d &tf)
	Embed a SE2 transform into 3D space with zero Z translation and only rotation about the Z axis. More...

template<class I , class S , typename = std::enable_if_t<ranges::input_iterator<I> && ranges::input_iterator<S>>>
	TupleVector (I, S) -> TupleVector< decay_tuple_like_t< ranges::iter_value_t< I >>>
	Deduction guide to construct from iterators. More...

	TYPED_TEST (BearingSensorModelTests, BullsEyeDetection)

	TYPED_TEST (BearingSensorModelTests, MapUpdate)

	TYPED_TEST (BearingSensorModelTests, MultipleBullsEyeDetections)

	TYPED_TEST (BearingSensorModelTests, NoSuchLandmark)

	TYPED_TEST (BearingSensorModelTests, OneStdInBearing)

	TYPED_TEST (BearingSensorModelTests, SmokeTest)

	TYPED_TEST (SparseGridTests, AllAccessorMethodsAreEquivalent)

	TYPED_TEST (SparseGridTests, CanBeConstructedEmpty)

	TYPED_TEST (SparseGridTests, DataAccessing)

	TYPED_TEST (SparseGridTests, NotPresent)

	TYPED_TEST (SparseGridTests, Resolution)

	TYPED_TEST (SparseGridTests, Size)

	TYPED_TEST_SUITE (BearingSensorModelTests, BearingSensorModelTestsTypes,)

	TYPED_TEST_SUITE (SparseGridTests, SparseGridTestCases,)

Variables
LandmarkMapBoundaries	default_map_boundaries {Eigen::Vector3d{-10.0, -10.0, 0.0}, Eigen::Vector3d{10.0, 10.0, 0.0}}

template<typename T , typename U >
constexpr bool	has_common_tuple_type_v = has_common_tuple_type<T, U>::value
	Convenience template variable for `has_common_tuple_type`. More...

template<class T , class TupleLike >
constexpr bool	has_single_element_v = has_single_element<T, TupleLike>::value
	Convenience template variable for `has_single_element`. More...

template<class T >
constexpr bool	is_tuple_like_v = is_tuple_like<T>::value
	Convenience template variable for `is_tuple_like`. More...

template<class Particle >
constexpr detail::make_from_state_fn< Particle >	make_from_state
	A function object to create a particle from a given state. More...

constexpr detail::make_policy_fn	make_policy
	Make policy function objects. More...

constexpr state_detail::state_fn	state
	Customization point object for accessing the `state` of a particle. More...

template<class T , class TupleLike >
constexpr std::size_t	tuple_index_v = tuple_index<T, TupleLike>::value
	Convenience template variable for `tuple_index`. More...

constexpr weight_detail::weight_fn	weight
	Customization point object for accessing the `weight` of a particle. More...

Detailed Description

The main Beluga namespace.

Typedef Documentation

◆ any_policy

template<class... Args>

using beluga::any_policy = typedef policy<std::function<bool(Args...)> >

Type erased policy.

Definition at line 129 of file policy.hpp.

◆ BaseRegularGrid2

template<typename Derived >

using beluga::BaseRegularGrid2 = typedef BaseRegularGrid<Derived, 2>

Convenience alias for a 2D base regular grid.

Definition at line 102 of file regular_grid.hpp.

◆ BaseRegularGrid3

template<typename Derived >

using beluga::BaseRegularGrid3 = typedef BaseRegularGrid<Derived, 3>

Convenience alias for a 3D base regular grid.

Definition at line 106 of file regular_grid.hpp.

◆ BearingSensorModel2d

template<class LandmarkMap >

using beluga::BearingSensorModel2d = typedef BearingSensorModel<LandmarkMap, Sophus::SE2d>

Sensor model based on discrete landmarks bearing detection for 2D state types.

Definition at line 153 of file bearing_sensor_model.hpp.

◆ BearingSensorModel3d

template<class LandmarkMap >

using beluga::BearingSensorModel3d = typedef BearingSensorModel<LandmarkMap, Sophus::SE3d>

Sensor model based on discrete landmarks bearing detection for 3D state types.

Definition at line 157 of file bearing_sensor_model.hpp.

◆ BearingSensorModelTestsTypes

using beluga::BearingSensorModelTestsTypes = typedef ::testing::Types<Sensor2D, Sensor3D>

Definition at line 80 of file test_bearing_sensor_model.cpp.

◆ Cluster

using beluga::Cluster = typedef Numeric<std::size_t, struct ClusterTag>

Cluster type, as a strongly typed std::size_t.

Definition at line 59 of file primitives.hpp.

◆ common_tuple_type_t

template<typename T , typename U >

using beluga::common_tuple_type_t = typedef typename common_tuple_type<T, U>::type

Convenience template type alias for common_tuple_type.

Definition at line 78 of file tuple_traits.hpp.

◆ decay_tuple_like_t

template<class T >

using beluga::decay_tuple_like_t = typedef typename decay_tuple_like<T>::type

Convenience template type alias for decay_tuple_like.

Definition at line 187 of file tuple_traits.hpp.

◆ DifferentialDriveModel2d

using beluga::DifferentialDriveModel2d = typedef DifferentialDriveModel<Sophus::SE2d>

Alias for a 2D differential drive model, for convinience.

Definition at line 177 of file differential_drive_model.hpp.

◆ DifferentialDriveModel3d

using beluga::DifferentialDriveModel3d = typedef DifferentialDriveModel<Sophus::SE3d>

Alias for a 3D differential drive model, for convinience.

Definition at line 180 of file differential_drive_model.hpp.

◆ LandmarkBearing3

using beluga::LandmarkBearing3 = typedef Eigen::Vector3d

Bearing of a landmark in the sensor reference frame.

Definition at line 34 of file landmark_detection_types.hpp.

◆ LandmarkCategory

using beluga::LandmarkCategory = typedef uint32_t

Type used to represent landmark categories.

Definition at line 32 of file landmark_detection_types.hpp.

◆ LandmarkMapBoundaries

using beluga::LandmarkMapBoundaries = typedef Eigen::AlignedBox3d

Boundaries of a landmark map.

Definition at line 36 of file landmark_detection_types.hpp.

◆ LandmarkPosition3

using beluga::LandmarkPosition3 = typedef Eigen::Vector3d

Position of a landmark in the world reference frame.

Definition at line 33 of file landmark_detection_types.hpp.

◆ LandmarkSensorModel2d

template<class LandmarkMap >

using beluga::LandmarkSensorModel2d = typedef LandmarkSensorModel<LandmarkMap, Sophus::SE2d>

Sensor model based on discrete landmarks for 2D state types.

Definition at line 168 of file landmark_sensor_model.hpp.

◆ LandmarkSensorModel3d

template<class LandmarkMap >

using beluga::LandmarkSensorModel3d = typedef LandmarkSensorModel<LandmarkMap, Sophus::SE3d>

Sensor model based on discrete landmarks for 3D state types.

Definition at line 172 of file landmark_sensor_model.hpp.

◆ NDTCell2d

using beluga::NDTCell2d = typedef NDTCell<2, double>

Convenience alias for a 2D NDT cell with double representation.

Definition at line 80 of file ndt_cell.hpp.

◆ NDTCell2f

using beluga::NDTCell2f = typedef NDTCell<2, float>

Convenience alias for a 2D NDT cell with float representation.

Definition at line 82 of file ndt_cell.hpp.

◆ NDTCell3d

using beluga::NDTCell3d = typedef NDTCell<3, double>

Convenience alias for a 3D NDT cell with double representation.

Definition at line 84 of file ndt_cell.hpp.

◆ NDTCell3f

using beluga::NDTCell3f = typedef NDTCell<3, float>

Convenience alias for a 3D NDT cell with float representation.

Definition at line 86 of file ndt_cell.hpp.

◆ NDTModelParam2d

using beluga::NDTModelParam2d = typedef NDTModelParam<2>

Convenience alias for a 2d parameters struct for the NDT sensor model.

Definition at line 161 of file ndt_sensor_model.hpp.

◆ NDTModelParam3d

using beluga::NDTModelParam3d = typedef NDTModelParam<3>

Convenience alias for a 3d parameters struct for the NDT sensor model.

Definition at line 164 of file ndt_sensor_model.hpp.

◆ RollingWindow

template<typename T , std::size_t N>

using beluga::RollingWindow = typedef CircularArray< T, N, CircularArrayFeatureFlags::kRolloverOnWrite | CircularArrayFeatureFlags::kExtrapolateOnRead | CircularArrayFeatureFlags::kLayoutReversal>

Convenient type alias for a circular array that behaves like a rolling window.

A rolling window automatically overwrites older values, it extrapolates its last value to always seem full, and it is accessed by the front.

Definition at line 465 of file circular_array.hpp.

◆ Sensor2D

using beluga::Sensor2D = typedef beluga::BearingSensorModel2d<LandmarkMap>

Definition at line 39 of file test_bearing_sensor_model.cpp.

◆ Sensor3D

using beluga::Sensor3D = typedef beluga::BearingSensorModel3d<LandmarkMap>

Definition at line 40 of file test_bearing_sensor_model.cpp.

◆ SparseGridTestCases

using beluga::SparseGridTestCases = typedef testing::Types<std::unordered_map<Eigen::Vector2i, int, Hasher>, std::map<Eigen::Vector2i, int, Less> >

Definition at line 42 of file test_sparse_value_grid.cpp.

◆ SparseValueGrid2

template<typename MapType >

using beluga::SparseValueGrid2 = typedef SparseValueGrid<MapType, 2>

Convenience alias for 2D sparse value grids.

Definition at line 97 of file sparse_value_grid.hpp.

◆ SparseValueGrid3

template<typename MapType >

using beluga::SparseValueGrid3 = typedef SparseValueGrid<MapType, 3>

Convenience alias for 3D sparse value grids.

Definition at line 101 of file sparse_value_grid.hpp.

◆ state_t

template<class T >

using beluga::state_t = typedef typename particle_traits<T>::state_type

Type trait that returns the state type given a particle type.

Definition at line 40 of file particle_traits.hpp.

◆ strong_double

using beluga::strong_double = typedef Numeric<double, struct StrongDoubleTag>

Definition at line 24 of file test_strongly_typed_numeric.cpp.

◆ strong_size_t

using beluga::strong_size_t = typedef Numeric<std::size_t, struct StrongSizeTTag>

Definition at line 25 of file test_strongly_typed_numeric.cpp.

◆ strong_size_t_2

using beluga::strong_size_t_2 = typedef Numeric<std::size_t, struct StrongSizeTTag2>

Definition at line 26 of file test_strongly_typed_numeric.cpp.

◆ tuple_index_t

template<class T , class TupleLike >

using beluga::tuple_index_t = typedef typename tuple_index<T, TupleLike>::type

Convenience template type alias for tuple_index.

Definition at line 149 of file tuple_traits.hpp.

◆ UUT

using beluga::UUT = typedef beluga::BeamSensorModel<StaticOccupancyGrid<5, 5> >

Definition at line 27 of file test_beam_model.cpp.

◆ Vector

template<class T >

using beluga::Vector = typedef std::vector<T, std::allocator<T> >

Shorthand for a vector with the default allocator.

Definition at line 216 of file tuple_vector.hpp.

◆ Weight

using beluga::Weight = typedef Numeric<double, struct WeightTag>

Weight type, as a strongly typed double.

Definition at line 56 of file primitives.hpp.

◆ weight_t

template<class T >

using beluga::weight_t = typedef typename particle_traits<T>::weight_type

Type trait that returns the weight type given a particle type.

Definition at line 44 of file particle_traits.hpp.

Enumeration Type Documentation

◆ CircularArrayFeatureFlags

enum beluga::CircularArrayFeatureFlags : std::int8_t

strong

Feature flags for circular arrays.

Enumerator
kNone
kRolloverOnWrite	! If enabled, older values in the array are overwritten by newer values if the array has reached its maximum size already.
kExtrapolateOnRead	! If enabled, the back value is extrapolated for constant accesses up to the array maximum size.
kLayoutReversal	! If enabled, the circular array memory layout is reversed so that values can be pushed to the front rather than the back of the array.

Definition at line 35 of file circular_array.hpp.

Function Documentation

◆ calculate_covariance() [1/2]

template<class Range , class Scalar >

Sophus::Matrix2<Scalar> beluga::calculate_covariance	(	Range &&	range,
		const Sophus::Vector2< Scalar > &	mean
	)

Convenience overload that calculates the covariance of a range given its mean for the case where all samples have the same weight.

Template Parameters

Range	A sized range type whose value type is `Sophus::Vector2<Scalar>`.
Scalar	A scalar type, e.g. double or float.

Parameters

range	Range to be used to calculate the covariance.
mean	The previously calculated mean of range. The value must be correct for the resulting covariance to be correct.

Returns: The calculated covariance, as a Sophus::Matrix2<Scalar>.

Definition at line 97 of file estimation.hpp.

◆ calculate_covariance() [2/2]

template<class Range , class WeightsRange , class Scalar >

Sophus::Matrix2<Scalar> beluga::calculate_covariance	(	Range &&	range,
		WeightsRange &&	normalized_weights,
		const Sophus::Vector2< Scalar > &	mean
	)

Calculates the covariance of a range given its mean and the weights of each element.

Template Parameters

Range	A sized range type whose value type is `Sophus::Vector2<Scalar>`.
WeightsRange	A sized range type whose value type is `Scalar`.
Scalar	A scalar type, e.g. double or float.

Parameters

range	Range to be used to calculate the covariance.
normalized_weights	Range with the normalized (total weight 1.0) weights of the samples in 'range'.
mean	The previously calculated mean of range. The value must be correct for the resulting covariance to be correct.

Returns: The calculated covariance, as a Sophus::Matrix2<Scalar>.

Definition at line 53 of file estimation.hpp.

◆ cluster_based_estimate()

template<class States , class Weights >

auto beluga::cluster_based_estimate	(	States &&	states,
		Weights &&	weights,
		ParticleClusterizerParam	parameters = `{}`
	)

Computes a cluster-based estimate from a particle set.

Particles are grouped into clusters around local maxima. The state mean and covariance of the cluster with the highest total weight is returned. If no clusters are found, the overall mean and covariance of the particles are calculated and returned.

Template Parameters

States	Range type of the states.
Weights	Range type of the weights.

Parameters

states	Range containing the states of the particles.
weights	Range containing the weights of the particles.
parameters	Parameters for the particle clusterizer (optional).

Returns: A pair consisting of the state mean and covariance of the cluster with the highest total weight.

Definition at line 414 of file cluster_based_estimation.hpp.

◆ effective_sample_size()

template<class Range , std::enable_if_t<!is_particle_range_v< Range >, int > = 0>

auto beluga::effective_sample_size ( Range && range )

Calculate the ESS of a given a range of weights.

Overload for particle ranges.

The effective sample size (ESS) is a figure of merit for importance sampling methods' output. It characterizes how well the target posterior distribution is approximated (as it is proportional to the efficiency of distribution parameter estimators, [kong1994sequentialimputations], section 4.1). It can be interpreted as the number of samples effectively approximating the distribution, and thus comparing with the total number of samples makes for a good mechanism to detect and react to performance degradation e.g. triggering a resample when the ESS falls below a fraction of the total number of samples.

The algorithm is based on [grisetti2007selectiveresampling], according to the description given in [tiacheng2015resamplingmethods].

Template Parameters

Range A forward range.

Parameters

range The range of weights.

Definition at line 46 of file effective_sample_size.hpp.

◆ element()

template<class T , class TupleLike >

constexpr decltype(auto) beluga::element ( TupleLike && tuple )

constexprnoexcept

Returns element of a tuple like object whose type is T (or a possibly const reference to T).

Definition at line 169 of file tuple_traits.hpp.

◆ estimate() [1/3]

template<class Poses , class Pose = ranges::range_value_t<Poses>, class Scalar = typename Pose::Scalar, typename = std::enable_if_t<std::is_same_v<Pose, typename Sophus::SE2<Scalar>>>>

std::pair<Sophus::SE2<Scalar>, Sophus::Matrix3<Scalar> > beluga::estimate ( Poses && poses )

Returns a pair consisting of the estimated mean pose and its covariance.

Given a range of poses, computes the estimated pose by averaging the translation and rotation parts, assuming all poses are equally weighted. Computes the covariance matrix of the translation parts and the circular variance of the rotation angles to create a 3x3 covariance matrix. It does not take into account the particle weights. This is appropriate for use with filter update cycles that resample the particle set at every iteration, since in that case the belief is fully represented by the spatial distribution of the particles, and the particle weights provide no additional information.

Template Parameters

Poses	A sized range type whose value type is `Sophus::SE2<Scalar>`.
Pose	The pose value type of the given range.
Scalar	A scalar type, e.g. double or float.

Parameters

poses Poses of equally weighted 2D poses.

Returns: The estimated pose and its 3x3 covariance matrix.

Definition at line 240 of file estimation.hpp.

◆ estimate() [2/3]

template<class Poses , class Weights , class Pose = ranges::range_value_t<Poses>, class Scalar = typename Pose::Scalar, typename = std::enable_if_t<std::is_same_v<Pose, typename Sophus::SE2<Scalar>>>>

std::pair<Sophus::SE2<Scalar>, Sophus::Matrix3<Scalar> > beluga::estimate	(	Poses &&	poses,
		Weights &&	weights
	)

Returns a pair consisting of the estimated mean pose and its covariance.

Given a range of poses, computes the estimated pose by averaging the translation and rotation parts. Computes the covariance matrix of the translation parts and the circular variance of the rotation angles to create a 3x3 covariance matrix. It does not take into account the particle weights.

Template Parameters

Poses	A sized range type whose value type is `Sophus::SE2<Scalar>`.
Weights	A sized range type whose value type is `Scalar`.
Pose	The pose value type of the given range.
Scalar	A scalar type, e.g. double or float.

Parameters

poses	Poses of equally weighted 2D poses.
weights	Poses of equally weighted 2D poses.

Returns: The estimated pose and its 3x3 covariance matrix.

Definition at line 129 of file estimation.hpp.

◆ estimate() [3/3]

template<class Scalars , class Weights , class Scalar = ranges::range_value_t<Scalars>, typename = std::enable_if_t<std::is_arithmetic_v<Scalar>>>

std::pair<Scalar, Scalar> beluga::estimate	(	Scalars &&	scalars,
		Weights &&	weights
	)

Computes mean and variance of a range of weighted scalars.

Given a range of scalars, computes the scalar mean and variance.

Template Parameters

Scalars	A sized range type whose value type is `std::vector<Scalar>`.
Weights	A sized range type whose value type is `Scalar`.
Scalar	The scalar value type of the given range of Scalars.

Parameters

scalars	Range of scalars.
weights	Range of weights.

Returns: The estimated mean and variance.

Definition at line 193 of file estimation.hpp.

◆ estimate_clusters()

template<class States , class Weights , class Clusters >

auto beluga::estimate_clusters	(	States &&	states,
		Weights &&	weights,
		Clusters &&	clusters
	)

For each cluster, estimate the mean and covariance of the states that belong to it.

Template Parameters

States	Range type of the states.
Weights	Range type of the weights.
Hashes	Range type of the hashes.

Parameters

states	Range containing the states of the particles.
weights	Range containing the weights of the particles.
clusters	Cluster ids of the particles.

Returns: A vector of elements, containing the weight, mean and covariance of each cluster, in no particular order.

Convenient factory method to pass to zip_with.

Definition at line 336 of file cluster_based_estimation.hpp.

◆ expected_aggregate_probability()

double beluga::expected_aggregate_probability ( std::vector< double > landmark_probs )

Definition at line 44 of file test_bearing_sensor_model.cpp.

◆ forward_like()

template<class T , class U >

constexpr auto&& beluga::forward_like ( U && value )

constexprnoexcept

Returns a reference to a value which has similar properties to T&&.

Implementation taken from https://en.cppreference.com/w/cpp/utility/forward_like since this feature is only available starting with C++23.

The program is ill-formed if T&& is not a valid type.

Template Parameters

T	The type from which to take the properties.
U	The type of the input value.

Parameters

value A value that needs to be forwarded like type T.

Returns: A reference to value of the determined type.

Definition at line 33 of file forward_like.hpp.

◆ get() [1/4]

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>

constexpr T&& beluga::get ( CircularArray< T, N, F > && array )

constexprnoexcept

Gets an rvalue reference to the ith value in a given array.

This is an std::get overload that relies on argument-dependent lookup (ADL).

Definition at line 496 of file circular_array.hpp.

◆ get() [2/4]

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>

constexpr T& beluga::get ( CircularArray< T, N, F > & array )

constexprnoexcept

Gets an lvalue reference to the ith value in a given array.

This is an std::get overload that relies on argument-dependent lookup (ADL).

Definition at line 487 of file circular_array.hpp.

◆ get() [3/4]

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>

constexpr const T&& beluga::get ( const CircularArray< T, N, F > && array )

constexprnoexcept

Gets a constant rvalue reference to the ith value in a given array.

This is an std::get overload that relies on argument-dependent lookup (ADL).

Definition at line 514 of file circular_array.hpp.

◆ get() [4/4]

template<std::size_t I, class T , std::size_t N, CircularArrayFeatureFlags F>

constexpr const T& beluga::get ( const CircularArray< T, N, F > & array )

constexprnoexcept

Gets a constant lvalue reference to the ith value in a given array.

This is an std::get overload that relies on argument-dependent lookup (ADL).

Definition at line 505 of file circular_array.hpp.

◆ get_default_model_params()

BearingModelParam beluga::get_default_model_params ( )

Definition at line 49 of file test_bearing_sensor_model.cpp.

◆ get_robot_pose_in_world()

template<typename T >

T beluga::get_robot_pose_in_world ( )

◆ get_robot_pose_in_world< Sophus::SE2d >()

template<>

Sophus::SE2d beluga::get_robot_pose_in_world< Sophus::SE2d > ( )

Definition at line 68 of file test_bearing_sensor_model.cpp.

◆ get_robot_pose_in_world< Sophus::SE3d >()

template<>

Sophus::SE3d beluga::get_robot_pose_in_world< Sophus::SE3d > ( )

Definition at line 73 of file test_bearing_sensor_model.cpp.

◆ GetParams()

BeamModelParam beluga::GetParams ( )

Definition at line 29 of file test_beam_model.cpp.

◆ kld_condition()

auto beluga::kld_condition	(	std::size_t	min,
		double	epsilon,
		double	z = `beluga::detail::kDefaultKldZ`
	)

inline

Returns a callable object that verifies if the KLD condition is being satisfied.

The callable object will compute the minimum number of samples based on a Kullback-Leibler distance epsilon between the maximum likelihood estimate and the true distribution.
Z is the upper standard normal quantile for P, where P is the probability that the error in the estimated distribution will be less than epsilon.

Here are some examples:

P	Z
0.900	1.28155156327703
0.950	1.64485362793663
0.990	2.32634787735669
0.999	3.09023224677087

If the computed value is less than what the min argument specifies, then min will be returned.

See KLD-Sampling: Adaptive Particle Filters [fox2001adaptivekldsampling].

Parameters

min	Minimum number of particles that the callable object will return.
epsilon	Maximum distance epsilon between the maximum likelihood estimate and the true distrubution.
z	Upper standard normal quantile for the probability that the error in the estimated distribution is less than epsilon.

Returns: A callable object with prototype (std::size_t hash) -> bool. hash is the spatial hash of the particle being added.
The returned callable object is stateful, tracking the total number of particles and the particle clusters based on the spatial hash.
The return value of the callable will be false when the number of particles is more than the minimum and the KLD condition is satisfied, if not it will be true.
i.e. A return value of true means that you need to keep sampling to satisfy the condition.

Definition at line 72 of file take_while_kld.hpp.

◆ make_sensor_data()

auto beluga::make_sensor_data ( std::vector< std::tuple< double, double, double, uint32_t >> detections )

Definition at line 56 of file test_bearing_sensor_model.cpp.

◆ MultivariateNormalDistribution()

template<class T >

beluga::MultivariateNormalDistribution	(	const T &	,
		const typename multivariate_distribution_traits< T >::covariance_type &
	)		-> MultivariateNormalDistribution< typename multivariate_distribution_traits< T >::result_type >

Deduction guide to deduce the correct result type.

◆ MultivariateUniformDistribution() [1/3]

template<class Derived >

beluga::MultivariateUniformDistribution ( const BaseOccupancyGrid2< Derived > & ) -> MultivariateUniformDistribution< Sophus::SE2d, Derived >

Deduction guide for 2D occupancy grids.

◆ MultivariateUniformDistribution() [2/3]

beluga::MultivariateUniformDistribution ( const Eigen::AlignedBox2d & ) -> MultivariateUniformDistribution< Sophus::SE2d, Eigen::AlignedBox2d >

Deduction guide for bounding regions in SE2 space.

◆ MultivariateUniformDistribution() [3/3]

beluga::MultivariateUniformDistribution ( const Eigen::AlignedBox3d & ) -> MultivariateUniformDistribution< Sophus::SE3d, Eigen::AlignedBox3d >

Deduction guide for bounding regions in SE3 space.

◆ nearest_obstacle_distance_map()

template<class Range , class DistanceFunction , class NeighborsFunction >

auto beluga::nearest_obstacle_distance_map	(	Range &&	obstacle_map,
		DistanceFunction &&	distance_function,
		NeighborsFunction &&	neighbors_function
	)

Returns a map where the value of each cell is the distance to the nearest obstacle.

The algorithm uses O(N) time and memory, where N=ranges::size(obstacle_map).

Template Parameters

Range	A sized range. Its value type must be bool.
DistanceFunction	A callable type, its prototype must be (std::size_t, std::size_t) -> DistanceType. DistanceType must be an scalar type.
NeighborsFunction	A callabe type, its prototype must be (std::size_t) -> NeighborsT, where NeighborsT is a Range with value type std::size_t.

Parameters

obstacle_map	A map that represents obstacles in an environment. If the value of a cell is True, the cell has an obstacle.
distance_function	Given the indexes of two cells in the map i and j, obstacle_map(i, j) must return the distance between the two cells.
neighbors_function	Given the index i of one cell in the map, neighbors_function(i) returns the cell indexes of neighbor cells in the obstacle map.

Returns: A map where each cell value is the distance to the nearest object.

Definition at line 54 of file distance_map.hpp.

◆ operator&()

constexpr bool beluga::operator&	(	CircularArrayFeatureFlags	mask,
		CircularArrayFeatureFlags	flag
	)

constexpr

Bitwise AND operator overload to check of the presence of a feature flag in a feature mask.

Definition at line 53 of file circular_array.hpp.

◆ operator<<()

template<typename T , std::size_t N, CircularArrayFeatureFlags F>

CircularArray<T, N, F>& beluga::operator<<	(	CircularArray< T, N, F > &	array,
		T	value
	)

Convenient stream operator overload to push a value to a circular array.

Functionally equivalent to a push_back() (or push_front() if the layout reversal feature is enabled).

Definition at line 473 of file circular_array.hpp.

◆ operator|()

constexpr CircularArrayFeatureFlags beluga::operator\|	(	CircularArrayFeatureFlags	lflag,
		CircularArrayFeatureFlags	rflag
	)

constexpr

Bitwise OR operator overload to combine two feature flags in a single mask-like flag.

Definition at line 46 of file circular_array.hpp.

◆ swap()

template<typename T , std::size_t N, CircularArrayFeatureFlags F, CircularArrayFeatureFlags G>

constexpr void beluga::swap	(	CircularArray< T, N, F > &	a,
		CircularArray< T, N, G > &	b
	)

constexpr

Swaps arrays a and b.

This is an std::swap overload that relies on argument-dependent lookup (ADL).

Definition at line 523 of file circular_array.hpp.

◆ TEST() [1/38]

beluga::TEST	(	BaselineRaycasting	,
		Nominal
	)

Definition at line 133 of file test_raycasting.cpp.

◆ TEST() [2/38]

beluga::TEST	(	BeamSensorModel	,
		GridUpdates
	)

Definition at line 84 of file test_beam_model.cpp.

◆ TEST() [3/38]

beluga::TEST	(	BeamSensorModel	,
		ImportanceWeight
	)

Definition at line 40 of file test_beam_model.cpp.

◆ TEST() [4/38]

beluga::TEST	(	Bresenham	,
		Modified
	)

Definition at line 127 of file test_bresenham.cpp.

◆ TEST() [5/38]

beluga::TEST	(	Bresenham	,
		MultiPassGuarantee
	)

Definition at line 27 of file test_bresenham.cpp.

◆ TEST() [6/38]

beluga::TEST	(	Bresenham	,
		Standard
	)

Definition at line 47 of file test_bresenham.cpp.

◆ TEST() [7/38]

beluga::TEST	(	Embed3DTests	,
		Se2ToSe3
	)

Definition at line 53 of file test_3d_embedding.cpp.

◆ TEST() [8/38]

beluga::TEST	(	Embed3DTests	,
		Se3ToSe2
	)

Definition at line 26 of file test_3d_embedding.cpp.

◆ TEST() [9/38]

beluga::TEST	(	NDTCellTests	,
		Ostream
	)

Definition at line 65 of file test_ndt_cell.cpp.

◆ TEST() [10/38]

beluga::TEST	(	NDTCellTests	,
		Product
	)

Definition at line 35 of file test_ndt_cell.cpp.

◆ TEST() [11/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		CanConstruct
	)

Definition at line 41 of file test_ndt_model.cpp.

◆ TEST() [12/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		FitPoints
	)

Definition at line 101 of file test_ndt_model.cpp.

◆ TEST() [13/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		Likelihoood
	)

Definition at line 66 of file test_ndt_model.cpp.

◆ TEST() [14/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		LoadFromHDF5HappyPath
	)

Definition at line 238 of file test_ndt_model.cpp.

◆ TEST() [15/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		LoadFromHDF5NonExistingFile
	)

Definition at line 243 of file test_ndt_model.cpp.

◆ TEST() [16/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		MinLikelihood
	)

Definition at line 45 of file test_ndt_model.cpp.

◆ TEST() [17/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		SensorModel
	)

Definition at line 213 of file test_ndt_model.cpp.

◆ TEST() [18/38]

beluga::TEST	(	NDTSensorModel2DTests	,
		ToCellsNotEnoughPointsInCell
	)

Definition at line 124 of file test_ndt_model.cpp.

◆ TEST() [19/38]

beluga::TEST	(	NDTSensorModel3DTests	,
		FitPoints
	)

Definition at line 146 of file test_ndt_model.cpp.

◆ TEST() [20/38]

beluga::TEST	(	NDTSensorModel3DTests	,
		LoadFromHDF5HappyPath
	)

Definition at line 251 of file test_ndt_model.cpp.

◆ TEST() [21/38]

beluga::TEST	(	NDTSensorModel3DTests	,
		LoadFromHDF5NonExistingFile
	)

Definition at line 247 of file test_ndt_model.cpp.

◆ TEST() [22/38]

beluga::TEST	(	NDTSensorModel3DTests	,
		SensorModel
	)

Definition at line 170 of file test_ndt_model.cpp.

◆ TEST() [23/38]

beluga::TEST	(	NDTSensorModel3DTests	,
		ToCellsNotEnoughPointsInCell
	)

Definition at line 135 of file test_ndt_model.cpp.

◆ TEST() [24/38]

beluga::TEST	(	NumericTypeDef	,
		hash
	)

Definition at line 49 of file test_strongly_typed_numeric.cpp.

◆ TEST() [25/38]

beluga::TEST	(	NumericTypeDef	,
		implicitCasts
	)

Definition at line 28 of file test_strongly_typed_numeric.cpp.

◆ TEST() [26/38]

beluga::TEST	(	NumericTypeDef	,
		numericLimits
	)

Definition at line 44 of file test_strongly_typed_numeric.cpp.

◆ TEST() [27/38]

beluga::TEST	(	Raycasting	,
		Nominal
	)

Definition at line 31 of file test_raycasting.cpp.

◆ TEST() [28/38]

beluga::TEST	(	Raycasting	,
		NonIdentityGridOrigin
	)

Definition at line 104 of file test_raycasting.cpp.

◆ TEST() [29/38]

beluga::TEST	(	TestAmclCore	,
		InitializeFromPose
	)

Definition at line 82 of file test_amcl_core.cpp.

◆ TEST() [30/38]

beluga::TEST	(	TestAmclCore	,
		InitializeWithNoParticles
	)

Definition at line 73 of file test_amcl_core.cpp.

◆ TEST() [31/38]

beluga::TEST	(	TestAmclCore	,
		ParticlesDependentRandomStateGenerator
	)

Definition at line 127 of file test_amcl_core.cpp.

◆ TEST() [32/38]

beluga::TEST	(	TestAmclCore	,
		SelectiveResampleCanBeConstructed
	)

Definition at line 164 of file test_amcl_core.cpp.

◆ TEST() [33/38]

beluga::TEST	(	TestAmclCore	,
		TestRandomParticlesInserting
	)

Definition at line 174 of file test_amcl_core.cpp.

◆ TEST() [34/38]

beluga::TEST	(	TestAmclCore	,
		Update
	)

Definition at line 77 of file test_amcl_core.cpp.

◆ TEST() [35/38]

beluga::TEST	(	TestAmclCore	,
		UpdateWithNoParticles
	)

Definition at line 88 of file test_amcl_core.cpp.

◆ TEST() [36/38]

beluga::TEST	(	TestAmclCore	,
		UpdateWithParticles
	)

Definition at line 95 of file test_amcl_core.cpp.

◆ TEST() [37/38]

beluga::TEST	(	TestAmclCore	,
		UpdateWithParticlesForced
	)

Definition at line 115 of file test_amcl_core.cpp.

◆ TEST() [38/38]

beluga::TEST	(	TestAmclCore	,
		UpdateWithParticlesNoMotion
	)

Definition at line 104 of file test_amcl_core.cpp.

◆ To2d()

Sophus::SE2d beluga::To2d ( const Sophus::SE3d & tf )

inline

Transforms a SE3 transform into a SE2 transform, by flattening the Z axis.

Definition at line 24 of file 3d_embedding.hpp.

◆ To3d()

Sophus::SE3d beluga::To3d ( const Sophus::SE2d & tf )

inline

Embed a SE2 transform into 3D space with zero Z translation and only rotation about the Z axis.

Definition at line 29 of file 3d_embedding.hpp.

◆ TupleVector()

template<class I , class S , typename = std::enable_if_t<ranges::input_iterator<I> && ranges::input_iterator<S>>>

beluga::TupleVector	(	I	,
		S
	)		-> TupleVector< decay_tuple_like_t< ranges::iter_value_t< I >>>

Deduction guide to construct from iterators.

◆ TYPED_TEST() [1/12]

beluga::TYPED_TEST	(	BearingSensorModelTests	,
		BullsEyeDetection
	)

Definition at line 89 of file test_bearing_sensor_model.cpp.

◆ TYPED_TEST() [2/12]

beluga::TYPED_TEST	(	BearingSensorModelTests	,
		MapUpdate
	)

Definition at line 98 of file test_bearing_sensor_model.cpp.

◆ TYPED_TEST() [3/12]

beluga::TYPED_TEST	(	BearingSensorModelTests	,
		MultipleBullsEyeDetections
	)

Definition at line 110 of file test_bearing_sensor_model.cpp.

◆ TYPED_TEST() [4/12]

beluga::TYPED_TEST	(	BearingSensorModelTests	,
		NoSuchLandmark
	)

Definition at line 164 of file test_bearing_sensor_model.cpp.

◆ TYPED_TEST() [5/12]

beluga::TYPED_TEST	(	BearingSensorModelTests	,
		OneStdInBearing
	)

Definition at line 132 of file test_bearing_sensor_model.cpp.

◆ TYPED_TEST() [6/12]

beluga::TYPED_TEST	(	BearingSensorModelTests	,
		SmokeTest
	)

Definition at line 84 of file test_bearing_sensor_model.cpp.

◆ TYPED_TEST() [7/12]

beluga::TYPED_TEST	(	SparseGridTests	,
		AllAccessorMethodsAreEquivalent
	)

Definition at line 112 of file test_sparse_value_grid.cpp.

◆ TYPED_TEST() [8/12]

beluga::TYPED_TEST	(	SparseGridTests	,
		CanBeConstructedEmpty
	)

Definition at line 46 of file test_sparse_value_grid.cpp.

◆ TYPED_TEST() [9/12]

beluga::TYPED_TEST	(	SparseGridTests	,
		DataAccessing
	)

Definition at line 84 of file test_sparse_value_grid.cpp.

◆ TYPED_TEST() [10/12]

beluga::TYPED_TEST	(	SparseGridTests	,
		NotPresent
	)

Definition at line 63 of file test_sparse_value_grid.cpp.

◆ TYPED_TEST() [11/12]

beluga::TYPED_TEST	(	SparseGridTests	,
		Resolution
	)

Definition at line 73 of file test_sparse_value_grid.cpp.

◆ TYPED_TEST() [12/12]

beluga::TYPED_TEST	(	SparseGridTests	,
		Size
	)

Definition at line 51 of file test_sparse_value_grid.cpp.

◆ TYPED_TEST_SUITE() [1/2]

beluga::TYPED_TEST_SUITE	(	BearingSensorModelTests	,
		BearingSensorModelTestsTypes
	)

◆ TYPED_TEST_SUITE() [2/2]

beluga::TYPED_TEST_SUITE	(	SparseGridTests	,
		SparseGridTestCases
	)

Variable Documentation

◆ default_map_boundaries

LandmarkMapBoundaries beluga::default_map_boundaries {Eigen::Vector3d{-10.0, -10.0, 0.0}, Eigen::Vector3d{10.0, 10.0, 0.0}}

Definition at line 42 of file test_bearing_sensor_model.cpp.

◆ has_common_tuple_type_v

template<typename T , typename U >

constexpr bool beluga::has_common_tuple_type_v = has_common_tuple_type<T, U>::value

inlineconstexpr

Convenience template variable for has_common_tuple_type.

Definition at line 90 of file tuple_traits.hpp.

◆ has_single_element_v

template<class T , class TupleLike >

constexpr bool beluga::has_single_element_v = has_single_element<T, TupleLike>::value

inlineconstexpr

Convenience template variable for has_single_element.

Definition at line 165 of file tuple_traits.hpp.

◆ is_tuple_like_v

template<class T >

constexpr bool beluga::is_tuple_like_v = is_tuple_like<T>::value

inlineconstexpr

Convenience template variable for is_tuple_like.

Definition at line 53 of file tuple_traits.hpp.

◆ make_from_state

template<class Particle >

constexpr detail::make_from_state_fn<Particle> beluga::make_from_state

inlineconstexpr

A function object to create a particle from a given state.

Takes a state and returns a new particle with that state. The new particle is given a weight of 1.

Template Parameters

Particle The particle type to be created.

Definition at line 120 of file particle_traits.hpp.

◆ make_policy

constexpr detail::make_policy_fn beluga::make_policy

inlineconstexpr

Make policy function objects.

Definition at line 42 of file policy.hpp.

◆ state

constexpr state_detail::state_fn beluga::state

inlineconstexpr

Customization point object for accessing the state of a particle.

Definition at line 163 of file primitives.hpp.

◆ tuple_index_v

template<class T , class TupleLike >

constexpr std::size_t beluga::tuple_index_v = tuple_index<T, TupleLike>::value

inlineconstexpr

Convenience template variable for tuple_index.

Definition at line 145 of file tuple_traits.hpp.

◆ weight

constexpr weight_detail::weight_fn beluga::weight

inlineconstexpr

Customization point object for accessing the weight of a particle.

Definition at line 264 of file primitives.hpp.

Namespaces

Classes

Typedefs

Enumerations

Functions

Variables

Detailed Description

Typedef Documentation

◆ any_policy

◆ BaseRegularGrid2

◆ BaseRegularGrid3

◆ BearingSensorModel2d

◆ BearingSensorModel3d

◆ BearingSensorModelTestsTypes

◆ Cluster

◆ common_tuple_type_t

◆ decay_tuple_like_t

◆ DifferentialDriveModel2d

◆ DifferentialDriveModel3d

◆ LandmarkBearing3

◆ LandmarkCategory

◆ LandmarkMapBoundaries

◆ LandmarkPosition3

◆ LandmarkSensorModel2d

◆ LandmarkSensorModel3d

◆ NDTCell2d

◆ NDTCell2f

◆ NDTCell3d

◆ NDTCell3f

◆ NDTModelParam2d

◆ NDTModelParam3d

◆ RollingWindow

◆ Sensor2D

◆ Sensor3D

◆ SparseGridTestCases

◆ SparseValueGrid2

◆ SparseValueGrid3

◆ state_t

◆ strong_double

◆ strong_size_t

◆ strong_size_t_2

◆ tuple_index_t

◆ UUT

◆ Vector

◆ Weight

◆ weight_t

Enumeration Type Documentation

◆ CircularArrayFeatureFlags

Function Documentation

◆ calculate_covariance() [1/2]

◆ calculate_covariance() [2/2]

◆ cluster_based_estimate()

◆ effective_sample_size()

◆ element()

◆ estimate() [1/3]

◆ estimate() [2/3]

◆ estimate() [3/3]

◆ estimate_clusters()

◆ expected_aggregate_probability()

◆ forward_like()

◆ get() [1/4]

◆ get() [2/4]

◆ get() [3/4]

◆ get() [4/4]

◆ get_default_model_params()

◆ get_robot_pose_in_world()

◆ get_robot_pose_in_world< Sophus::SE2d >()

◆ get_robot_pose_in_world< Sophus::SE3d >()

◆ GetParams()

◆ kld_condition()

◆ make_sensor_data()

◆ MultivariateNormalDistribution()

◆ MultivariateUniformDistribution() [1/3]

◆ MultivariateUniformDistribution() [2/3]

◆ MultivariateUniformDistribution() [3/3]

◆ nearest_obstacle_distance_map()

◆ operator&()

◆ operator<<()

◆ operator|()

◆ swap()