ov_msckf Namespace Reference

Extended Kalman Filter estimator. More...

Classes
struct	NoiseManager
	Struct of our imu noise parameters. More...
class	Propagator
	Performs the state covariance and mean propagation using imu measurements. More...
class	ROS1Visualizer
	Helper class that will publish results onto the ROS framework. More...
class	ROS2Visualizer
	Helper class that will publish results onto the ROS framework. More...
class	ROSVisualizerHelper
	Helper class that handles some common versions into and out of ROS formats. More...
class	Simulator
	Master simulator class that generated visual-inertial measurements. More...
class	State
	State of our filter. More...
class	StateHelper
	Helper which manipulates the State and its covariance. More...
struct	StateOptions
	Struct which stores all our filter options. More...
class	UpdaterHelper
	Class that has helper functions for our updaters. More...
class	UpdaterMSCKF
	Will compute the system for our sparse features and update the filter. More...
struct	UpdaterOptions
	Struct which stores general updater options. More...
class	UpdaterSLAM
	Will compute the system for our sparse SLAM features and update the filter. More...
class	UpdaterZeroVelocity
	Will try to detect and then update using zero velocity assumption. More...
class	VioManager
	Core class that manages the entire system. More...
struct	VioManagerOptions
	Struct which stores all options needed for state estimation. More...

Detailed Description

Extended Kalman Filter estimator.

This is an implementation of a Multi-State Constraint Kalman Filter (MSCKF) [Mourikis2007ICRA] which leverages inertial and visual feature information. We want to stress that this is not a "vanilla" implementation of the filter and instead has many more features and improvements over the original. In additional we have a modular type system which allows us to initialize and marginalizing variables out of state with ease. Please see the following documentation pages for derivation details:

• IMU Propagation Derivations — Inertial propagation derivations and details.
• Measurement Update Derivations — General state update for the different measurements.
• First-Estimate Jacobian Estimators — Background on First-Estimate Jacobians and how we use them.
• Covariance Index Internals — High level details on how the type system and covariance management works.

The key features of the system are the following:

• Sliding stochastic imu clones
• First-Estimate Jacobians to maintain consistency
• Camera intrinsics and extrinsic online calibration
• Time offset between camera and imu calibration
• Inertial intrinsic calibration (including g-sensitivity)
• Standard MSCKF features with nullspace projection
• 3d SLAM feature support (with six different representations)
• Generic simulation of trajectories through and environment (see ov_msckf::Simulator)

We suggest those that are interested to first checkout the State and Propagator which should provide a nice introduction to the code. Both the slam and msckf features leverage the same Jacobian code, and thus we also recommend looking at the UpdaterHelper class for details on that.