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Design, analyze, and test lidar processing systems

Lidar Toolbox™ provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.

You can train custom detection and semantic segmentation models using deep learning and machine learning algorithms such as PointSeg, PointPillar, and SqueezeSegV2. The Lidar Labeler app supports manual and semi-automated labeling of lidar point clouds for training deep learning and machine learning models. The toolbox lets you stream data from Velodyne® lidars and read data recorded by Velodyne and IBEO lidar sensors.

Lidar Toolbox provides reference examples illustrating the use of lidar processing for perception and navigation workflows. Most toolbox algorithms support C/C++ code generation for integrating with existing code, desktop prototyping, and deployment.


About Lidar Processing

Featured Examples


What is Lidar Toolbox?
A brief introduction to the Lidar Toolbox.

Lidar Camera Calibration with MATLAB
An introduction to lidar camera calibration functionality, which is an essential step in combining data from lidar and a camera in a system.

Object Detection on Lidar Point Clouds Using Deep Learning
Learn how to use a PointPillars deep learning network for 3-D object detection on lidar point clouds.

Build a Collision Warning System with 2-D Lidar Using MATLAB
Build a system that can issue collision warnings based on 2-D lidar scans in a simulated warehouse arena.