Localization algorithms, like Monte Carlo localization and scan matching, estimate your pose in a known map using range sensor or lidar readings. Pose graphs track your estimated poses and can be optimized based on edge constraints and loop closures. For simultaneous localization and mapping, see SLAM.
|Localize robot using range sensor data and map|
|Create object for storing 2-D lidar scan|
|Get particles from localization algorithm|
|Create an odometry motion model|
|Create a likelihood field range sensor model|
|Create resampling policy object with resampling settings|
|Create 2-D pose graph|
|Create 3-D pose graph|
|3-D pose plot|
|Add landmark point node to pose graph|
|Add relative pose to pose graph|
|Edge node pairs in pose graph|
|Edge constraints in pose graph|
|Compute pose graph edge residual errors|
|Find edge ID of edge|
|Poses of nodes in pose graph|
|Optimize nodes in pose graph|
|Remove loop closure edges from graph|
|Plot pose graph|
|Optimize pose graph and remove bad loop closures|
|Compute Ackermann vehicle odometry using wheel encoder ticks and steering angle|
|Compute bicycle odometry using wheel encoder ticks and steering angle|
|Compute differential-drive vehicle odometry using wheel encoder ticks|
|Compute unicycle odometry using wheel encoder ticks and angular velocity|
matchScans function to compute the pose difference between a series of laser scans.
This example shows how to use an inertial measurement unit (IMU) to minimize the search range of the rotation angle for scan matching algorithms.
The Monte Carlo Localization (MCL) algorithm is used to estimate the position and orientation of a robot.
A particle filter is a recursive, Bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state.
To use the
stateEstimatorPF (Robotics System Toolbox) particle filter, you must specify parameters such as the number of particles, the initial particle location, and the state estimation method.