Design, analyze, and test lidar processing systems
Lidar Semantic Segmentation
Train, evaluate, and deploy semantic segmentation networks, including PointSeg and SqueezeSegV2, on lidar data.
Object Detection on Lidar Point Clouds
Detect and fit oriented bounding boxes around objects in lidar point clouds. Design, train, and evaluate robust detectors such as PointPillars networks.
Apply built-in or custom algorithms to automate lidar point cloud labeling with the Lidar Labeler App, and evaluate automation algorithm performance.
Lidar and Camera Calibration
Estimate the rigid transformation matrix between a lidar and a camera using Lidar Camera Calibrator App.
Lidar Processing Algorithms
Convert unorganized point clouds to organized point clouds. Apply functions and algorithms for ground segmentation, downsampling, median filtering, normal estimation, transforming point clouds, and extracting point cloud features.
Velodyne Lidar Sensor Acquisition
Acquire live lidar point clouds from Velodyne Lidar sensors, visualize them in MATLAB, and develop lidar sensing applications.
Reading and Writing Lidar Point Cloud Data
Read lidar data in different file formats, including PCAP, LAS, ibeo, PCD, and PLY. Write lidar data to PLY and PCD files.
Feature Extraction from Lidar Point Clouds
Extract fast point feature histogram (FPFH) descriptors from lidar point clouds.