What Is Lidar Toolbox? - MATLAB & Simulink
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    What Is Lidar Toolbox?

    Lidar Toolbox™ provides algorithms, functions, and apps for designing, analyzing, and testing lidar data processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, point cloud registration, and obstacle detection. Lidar Camera Calibrator app from Lidar Toolbox can be used to cross calibrate lidar and camera for workflows that combine computer vision and lidar data 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 point clouds for training deep learning and machine learning models. Lidar toolbox lets you stream data from Velodyne® and Ouster lidars and read data recorded by Velodyne, Ouster, and Hesai Pandar lidar sensors.

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

    Published: 3 Sep 2020

    Lidar Toolbox provides algorithms, functions, and an app for designing, analyzing, and testing Lidar processing systems. You can read, visualize, and process 2D and 3D point clouds, apply deep learning algorithms on Lidar point clouds, cross calibrate Lidar and camera sensors, and implement 3D SLAM algorithms for autonomous driving, aerial, and robotics applications.

    Lidar Toolbox enables you to read Lidar point clouds in different file formats. You can also stream live data from Velodyne Lidar sensors using the corresponding support package.

    The Lidar Viewer app enables visualization, analysis, and preprocessing of Lidar point clouds. You can interactively edit the point clouds and export it. You can also export and reuse the preprocessing operations performed in the app as a function script in the MATLAB workspace.

    The toolbox provides functionality to train, test, and deploy deep learning networks on Lidar point clouds for object detection and semantic segmentation.

    The Lidar Labeler or app simplifies ground truth labeling of Lidar point clouds. The app provides an interactive user interface that enables manual and semi-automated labeling of Lidar point clouds for training deep learning models.

    The Lidar Camera Calibrator app helps estimating the rotational and translational transformation between camera and Lidar in a system. This data can be used to fuse color information from a camera to a Lidar point cloud, or to transform bounding box coordinates between Lidar and camera.

    The toolbox also provides functionality to register Lidar data point clouds that help in implementing 3D SLAM from ground and aerial Lidar data. You can use fast point histogram features or segment matching methods to match features and register Lidar point cloud sequences to progressively build 3D maps.

    The toolbox also provides 2D Lidar processing workflows like real time collision warning, obstacle detection, and 2D Lidar SLAM.

    For more information about Lidar Toolbox, visit the product page.

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