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point cloud registration and map building, 2-d and 3-d slam, and 2-d obstacle detection

to understand an unknown environment and navigate to a desired destination, a robot must have a clear picture of its surroundings. especially in the absence of gps data, a simultaneous localization and mapping(slam) algorithm can help a robot make effective decisions and plan a path through its environment.

slam consists of these two processes:

localization — estimating the pose of the robot in a known environment.
mapping — building a map of an unknown environment by using a known robot pose and sensor data.

localization requires the robot to have a map of the environment, and mapping requires a good pose estimate. in the slam process, a robot creates a map of an environment while localizing itself. for more information, see .

to perform slam, you must preprocess point clouds. lidar toolbox™ provides functions to extract features from point clouds and use them to register point clouds to one another. for an example of how to use fast point feature histogram (fpfh) feature extraction in a 3-d slam workflow for aerial data, see .

you can also perform slam by using 2-d lidar scans. by storing the data for a 2-d lidar scan in a object, you can perform scan matching to estimate pose. for more information, see build map from 2-d lidar scans using slam.

lidar toolbox supports various graph-based slam workflows, including 2-d slam, 3-d slam, online slam and offline slam.

functions

detect, extract, and match features

	detect loam feature points from 3-d lidar data
	detect iss feature points in point cloud
	extract eigenvalue-based features from point cloud segments
	extract fast point feature histogram (fpfh) descriptors from point cloud
	find matching features between point clouds

register point clouds

	register two point clouds using loam algorithm
	register two point clouds using fgr algorithm
	register two point clouds using icp algorithm
	register two point clouds using cpd algorithm
	register two point clouds using phase correlation
	register two point clouds using ndt algorithm

estimate pose

	estimate pose between two laser scans
	estimate pose between two lidar scans using grid-based search
	estimate pose between two laser scans using line features
	transform laser scan based on relative pose

build map

	create map of loam feature points for map building
	map of segments and features for localization and loop closure detection

perform 2-d slam

	simultaneous localization and mapping using 2-d lidar scans
	add 2-d lidar scan to map
	detect loop closure in 2-d lidar scan map
	add loop closure to the map
	delete loop closure between 2-d lidar scans
	create 2-d pose graph from lidar scan map
	update absolute poses of 2-d lidar scans
	find absolute pose of 2-d lidar scan in the map
	create a copy of `lidarscanmap` object
	display 2-d lidar scans and lidar sensor trajectory

visualize results

	visualize difference between two point clouds
	visualize streaming 3-d point cloud data
	display point clouds with matched feature points

sensor model

	simulate range-bearing sensor readings
	simulate lidar sensor readings

storage

	create object for storing 2-d lidar scan
	object for storing eigenvalue-based features
	object for storing loam feature points

topics

understand point cloud registration and mapping workflow.
this example shows how to estimate a rigid transformation between two point clouds.
match and visualize corresponding features in point clouds
this example shows how to match corresponding features between point clouds using the pcmatchfeatures function and visualize them using the pcshowmatchedfeatures function.
this example shows you how to generate lidar point cloud data for a driving scene with roads, pedestrians, and vehicles.

featured examples

build map from 2-d lidar scans using slam

implement the slam algorithm on a series of 2-d lidar scans using scan processing and pose graph optimization (pgo). the goal of this example is to estimate the trajectory of the robot and build a map of the environment.