Class AMCLLocalizer
Defined in File AMCLLocalizer.hpp
Inheritance Relationships
Base Type
public LocalizerMethodBase
Class Documentation
A localization method implementing a simplified AMCL (Adaptive Monte Carlo Localization) approach.
Public Functions
Default constructor.
Destructor.
Initializes the localization method.
Sets up publishers, subscribers, and prepares the particle filter.
- Throws:
std::runtime_error – if initialization fails.
Real-time update of the localization state.
Used for time-critical update operations.
- Parameters:
nav_state – The current navigation state (read/write).
General update of the localization state.
May include operations not suitable for real-time execution.
- Parameters:
nav_state – The current navigation state (read/write).
Gets the current estimated pose as a transform.
- Returns:
The transform from map to base footprint frame.
Gets the current estimated pose as an Odometry message.
- Returns:
A nav_msgs::msg::Odometry message containing the estimated pose.
Protected Functions
Initializes the set of particles.
Publishes a TF transform between map and base footprint.
- Parameters:
map2bf – The transform to be published.
Publishes the current set of particles.
Publishes the estimated pose with covariance.
- Parameters:
est_pose – The estimated transform to be published.
Applies the motion model to update particle poses.
- Parameters:
nav_state – The current navigation state.
Applies the sensor model to update particle weights.
- Parameters:
nav_state – The current navigation state.
Re-initializes the particle cloud if necessary.
Callback for receiving odometry updates.
- Parameters:
msg – The incoming odometry message.
Update odom from TFs instead of a odom topic.
Protected Attributes
TF broadcaster to publish map to base_footprint transform.
Publisher for visualization of the particle cloud.
Publisher for the estimated robot pose with covariance.
Subscriber for odometry messages.
List of particles representing the belief distribution.
Random number generator used for sampling noise.
Current estimated odometry-based pose.
Translational noise standard deviation.
Rotational noise standard deviation.
Coupling noise between translation and rotation.
Minimum translation noise threshold.
Minimum yaw noise threshold.
Whether to use TFs to compute odom.
Last odometry transform received.
Previous odometry transform (used to compute deltas).
Flag indicating if the odometry has been initialized.
Time interval (in seconds) after which the particles should be reseeded.
Timestamp of the last reseed event.
Timestamp of the last input message (odometry or initial pose).
Internal static map.