Get Started with Lidar Toolbox

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.

Tutorials

• Read, Process, and Write Lidar Point Cloud Data
  

  This example shows how to read a point cloud into the workspace, select a desired set of points, and then write the selected points to a point cloud file format.

• Extract Ground Points and Non-Ground Points from Lidar Data
  

  Read lidar data from a PCAP file by using the velodyneFileReader function.

• Match and Visualize Corresponding Features in Point Clouds
  

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

• Estimate Transformation Between Two Point Clouds Using Features
  

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

About Lidar Processing

• Introduction to Lidar
  

  High-level overview of lidar concepts and applications.

• What Is Lidar-Camera Calibration?
  

  Fuse lidar and camera data.

• Implement Point Cloud SLAM in MATLAB
  

  Understand point cloud registration and mapping workflow.

• Deep Learning with Point Clouds
  

  Learn point cloud processing using deep learning.

Featured Examples

Determine Safe Landing Area for Aerial Vehicles

Demonstrates how to determine a safe landing area for aerial vehicles, such as helicopters and UAVs, by using aerial lidar data.

• Open Live Script

Motion Compensation in 3-D Lidar Point Clouds Using Sensor Fusion

Compensate point cloud distortion due to ego vehicle motion by fusing data from Global Positioning System (GPS) and inertial measurement unit (IMU) sensors. The goal of this example is to compensate the distortion in the point cloud data and recreate the surroundings accurately.

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Aerial Lidar Semantic Segmentation Using RandLANet Deep Learning

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

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Lidar Object Detection Using Complex-YOLO v4 Network

Detect objects in point clouds using you only look once version 4 (YOLO v4) deep learning network. In this example, you will

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Multi-Lidar Calibration

Calibrate multiple 3-D lidar sensors mounted on a vehicle to estimate a relative transformation between them. Traditional methods, such as marker-based registration, are difficult when the lidar sensors have a negligible overlap between their fields of view (FOVs). The calibration also becomes more difficult as the number of lidar sensors increases. This example demonstrates the use of the trajectories of individual lidar sensors to estimate the transformation between them. This method of calibration is also known as hand-eye calibration.

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Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

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

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Lidar 3-D Object Detection Using PointPillars Deep Learning

Train a PointPillars network for object detection in point clouds.

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Build Map from 2-D Lidar Scans Using SLAM

Implement the SLAM algorithm on a series of 2-D lidar scans using scan processing 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.

• Open Live Script

Videos

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.