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.

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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.

Tutorials

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• Read Lidar and Camera Data from Rosbag File
  

  This example shows how to read and save images and point cloud data from a rosbag file.

• Estimate Transformation Between Two Point Clouds Using Features
  

  This example shows how to estimate rigid transformation between two point clouds.

• Match and Visualize Corresponding Features in Point Clouds
  

  This example shows how to corresponding features between point clouds using the pcmatchfeatures function and visualize them using the pcshowMatchedFeatures function.

• Get Started with the Lidar Labeler
  

  Interactively label a point cloud or point cloud sequence.

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About Lidar Processing

• Lidar Processing Overview
  

  High-level overview of lidar applications.

• What Is Lidar Camera Calibration?
  

  Integrate lidar and camera data.

• Point Cloud SLAM Overview
  

  Understand point cloud registration and mapping workflow.

• Coordinate Systems in Lidar Toolbox
  

  Overview of coordinate systems in Lidar Toolbox.

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Featured Examples

Convert unorganized point clouds to organized format using spherical projection.

Train a PointNet++ deep learning network to perform semantic segmentation on aerial lidar data.

Detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle. The lidar data used in this example is recorded from a highway-driving scenario. In this example, 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 filter is used to track the detected vehicles.

Implement the simultaneous localization and mapping (SLAM) algorithm on a series of 2-D lidar scans using scan processing algorithms and pose graph optimization (PGO). The goal of this example is to estimate the trajectory of the robot and build a map of the environment.

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Demonstrates how to process 3-D lidar data from a sensor mounted on a vehicle to progressively build a map. Such a map is suitable for automated driving workflows such as localization and navigation. These maps can be used to localize a vehicle within a few centimeters.

Videos

How To See Matlab Toolboxes

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.