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Lidar Toolbox

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.

Get Started

Learn the basics of Lidar Toolbox

I/O

Read, write, and visualize lidar data

Preprocessing

Downsample, median filter, transform, extract features from, and align 3-D point clouds

Labeling, Segmentation, and Detection

Label, segment, detect, and track objects in point cloud data using deep learning and geometric algorithms

Calibration and Sensor Fusion

Interactively perform calibration, estimate lidar-camera transform, and fuse data from each sensor

Navigation and Mapping

Point cloud registration and map building, 2-D and 3-D SLAM, and 2-D obstacle detection

Lidar Toolbox Supported Hardware

Support for third-party hardware