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Navigation and Mapping

Point cloud registration and map building, 2-D and 3-D SLAM, and 2-D obstacle detection

A key component for advanced driver assistance systems (ADAS) applications and autonomous robots is enabling awareness of where the vehicle or robot is, with respect to its surroundings and using this information to estimate the best path to its destination. The simultaneous localization and mapping (SLAM) process uses algorithms to estimate the pose of a vehicle and the map of the environment at the same time.

Lidar Toolbox™ provides a point cloud registration workflow that uses the fast point feature histogram (FPFH) algorithm to stitch together point cloud sequences. You can use this feature for progressive map building. Such a map can facilitate path planning for vehicle navigation or can be used for SLAM. For an example of how to use the extractFPFHFeatures function in a 3-D SLAM workflow for aerial data, see Aerial Lidar SLAM Using FPFH Descriptors.

Lidar Toolbox also provides features for scan matching and simulating range-bearing sensor readings. These features are used in 2-D SLAM and obstacle detection workflows


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lidarscanmapSimultaneous localization and mapping using 2-D lidar scans
addScanAdd 2-D lidar scan to map
detectLoopClosureDetect loop closure in 2-D lidar scan map
addLoopClosureAdd loop closure to the map
deleteLoopClosureDelete loop closure between 2-D lidar scans
poseGraphCreate 2-D pose graph from lidar scan map
updateScanPosesUpdate absolute poses of 2-D lidar scans
findPoseFind absolute pose of 2-D lidar scan in the map
copyCreate a copy of lidarscanmap object
showDisplay 2-D lidar scans and lidar sensor trajectory
matchScansEstimate pose between two laser scans
matchScansGridEstimate pose between two lidar scans using grid-based search
matchScansLineEstimate pose between two laser scans using line features
transformScanTransform laser scan based on relative pose
rangeSensorSimulate range-bearing sensor readings
lidarSensorSimulate lidar sensor
lidarScanCreate object for storing 2-D lidar scan
eigenFeatureObject for storing eigenvalue-based features
LOAMPointsObject for storing LOAM feature points
pcregistericpRegister two point clouds using ICP algorithm
pcregistercpdRegister two point clouds using CPD algorithm
pcregistercorrRegister two point clouds using phase correlation
pcregisterndtRegister two point clouds using NDT algorithm
pcregisterloamRegister two point clouds using LOAM algorithm
pcmaploamCreate map of LOAM feature points for map building
detectLOAMFeaturesDetect LOAM feature points from 3-D lidar data
extractEigenFeaturesExtract eigenvalue-based features from point cloud segments
extractFPFHFeaturesExtract fast point feature histogram (FPFH) descriptors from point cloud
pcmatchfeaturesFind matching features between point clouds
pcmapsegmatchMap of segments and features for localization and loop closure detection
pcshowMatchedFeaturesDisplay point clouds with matched feature points