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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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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
lidarScanCreate object for storing 2-D lidar scan
eigenFeatureObject for storing eigenvalue-based features
pcregistericpRegister two point clouds using ICP algorithm
pcregistercpdRegister two point clouds using CPD algorithm
pcregisterndtRegister two point clouds using NDT algorithm
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


Implement Point Cloud SLAM in MATLAB

Understand point cloud registration and mapping workflow.

Estimate Transformation Between Two Point Clouds Using Features

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.

Featured Examples