Lidar is one of the most reliable sensors used for robotics perception and autonomous navigation. Its output can be used for both self-awareness and situational awareness. For example, a lidar point cloud can be processed to estimate the ego vehicle’s motion and detect and track nearby objects.

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In this video, the presenter walks you through two examples that show how to detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle.

The lidar data is recorded from a highway-driving scenario. The examples illustrate the workflow in MATLAB® for processing the point cloud and tracking the objects. 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 (IMM) filter is used to track the detected vehicles.

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