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2-d and 3-d simultaneous localization and mapping

simultaneous localization and mapping (slam) uses both mapping and localization and pose estimation algorithms to build a map and localize your vehicle in that map at the same time. use to tune your own slam algorithm that processes lidar scans and odometry pose estimates to iteratively build a map. use to take logged and filtered data to create a map using slam. the app lets you manually modify relative poses and align scans to improve the accuracy of your map.

apps

build 2-d grid maps using lidar-based slam

functions

ekfslam

	perform simultaneous localization and mapping using extended kalman filter
	correct state and state error covariance
	retrieve landmark information
	retrieve corrected and predicted pose history
	predict state and state error covariance
	remove landmark from state vector
	reset state and state estimation error covariance

lidarslam

	perform localization and mapping using lidar scans
	add scan to lidar slam map
	build occupancy map from lidar scans
	remove loop closures from pose graph
	extract scans and corresponding poses
	plot scans and robot poses

pose graphs

	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

factor graph

	bipartite graph of factors and nodes
	solver options for factor graph
	import factor graph from g2o log file
	convert imu readings to factor
	factor imu parameters
	factor for gps measurement
	factor relating two se(2) poses
	factor relating two se(3) poses
	factor relating se(2) position and 2-d point
	factor relating se(3) position and 3-d point
	prior factor for imu bias
	prior factor for 3-d velocity
	full-state prior factor for se(3) pose
	factor relating se(3) camera pose and 3-d point
	estimate gravity rotation using imu measurements and factor graph optimization
	estimate gravity rotation and pose scale using imu measurements and factor graph optimization

featured examples

perform slam using 3-d lidar point clouds

demonstrates how to implement the simultaneous localization and mapping (slam) algorithm on collected 3-d lidar sensor data using point cloud processing algorithms and pose graph optimization. the goal of this example is to estimate the trajectory of the robot and create a 3-d occupancy map of the environment from the 3-d lidar point clouds and estimated trajectory.

implement simultaneous localization and mapping (slam) with lidar scans

demonstrates how to implement the simultaneous localization and mapping (slam) algorithm on a collected series of lidar scans using pose graph optimization. the goal of this example is to build a map of the environment using the lidar scans and retrieve the trajectory of the robot.