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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. The toolbox provides workflows and an app for lidar-camera cross-calibration.

The toolbox lets you stream data from Velodyne®, Ouster®, and Hokuyo™ lidars and read data recorded by sensors such as Velodyne, Ouster, and Hesai® lidar sensors. The Lidar Viewer App enables interactive visualization and analysis of lidar point clouds. You can train detection, semantic segmentation, and classification models using machine learning and deep learning algorithms such as PointPillars, SqueezeSegV2, and PointNet++. The Lidar Labeler App supports manual and semi-automated labeling of lidar point clouds for training deep learning and machine learning models.

Lidar Toolbox provides lidar processing reference examples 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


A highway scenario for lidar data

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

Lidar camera calibration

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.

PointPillars detection results

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.

Collision warning system results

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.