Train a SqueezeSegV2 semantic segmentation network on 3-D organized lidar point cloud data.

Train a PointNet++ deep learning network to perform semantic segmentation on aerial lidar data.

Segment and classify terrain in aerial lidar data as ground, building, and vegetation. The example uses a LAZ file captured by an airborne lidar system as input. First, classify the point cloud data in the LAZ file into ground and non-ground points. Then, further classify non-ground points into building and vegetation points based on normals and curvature features. This figure provides an overview of the process.

Train a PointPillars network for object detection in point clouds.

Automate vehicle detections in a point cloud using a pretrained PointPillars object detection network in the Lidar Labeler app. In this example, you use the AutomationAlgorithm interface to automate labeling in the Lidar Labeler app.

Detect lanes in lidar point clouds. You can use the intensity values returned from lidar point clouds to detect ego vehicle lanes. You can further improve the lane detection by using a curve-fitting algorithm and tracking the curve parameters. Lidar lane detection enables you to build complex workflows like lane keep assist, lane departure warning, and adaptive cruise control for autonomous driving. A test vehicle collects the lidar data using a lidar sensor mounted on its rooftop.

Detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle. The lidar data used in this example is recorded from a highway-driving scenario. In this example, the point cloud data is segmented to determine the class of objects using the PointSeg network. A joint probabilistic data association (JPDA) tracker with an interactive multiple model filter is used to track the detected vehicles.