A key components 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 can be used in a 2-D obstacle detection workflow that provides collision warnings for vehicles in real-time.
|Register two point clouds using ICP algorithm|
|Register two point clouds using CPD algorithm|
|Register two point clouds using NDT algorithm|
|Extract fast point feature histogram (FPFH) descriptors from point cloud|
|Find matching features between point clouds|
|Display point clouds with matched feature points|