You can combine multiple point clouds to reconstruct a 3-D scene, or build a map with registered point clouds, detect loop closures, optimize the map to correct for drift, and perform localization in the prebuilt map.

Learn how to perform point cloud processing. Resources include examples, technical documentation, and user stories on how to leverage 3D point cloud data.

Getting Started with Point Clouds Using Deep Learning. Deep learning can automatically process point clouds for a wide range of 3-D imaging applications. Point clouds typically come from 3-D scanners, such as a lidar or Kinect ® devices. They have applications in robot navigation and perception, depth estimation, stereo vision, surveillance ...

This example demonstrates how to implement the simultaneous localization and mapping (SLAM) algorithm on collected 3-D lidar sensor data using point cloud processing algorithms and pose graph optimization. The goal of this example is to estimate the trajectory of the robot and create a 3-D occupancy map of the environment from the 3-D lidar ...

This example demonstrates how to stitch multiple point clouds to reconstruct a 3-D scene using ICP point cloud registration. It also shows how to leverage the color information present in the point clouds using ICP to improve the accuracy of the reconstructed scene.

Deep learning addresses various challenges in processing point cloud data. It is easier to perform complex point cloud processing tasks such as segmentation, detection, and tracking, by training deep learning networks.

Point cloud semantic segmentation or classification is a process of associating each point in a point cloud with a semantic label such as tree, person, road, vehicle, ocean, or building. Segmentation clusters points with similar characteristics into homogeneous regions.

The pointCloud object creates point cloud data from a set of points in 3-D coordinate system. The points generally represent the x , y , and z geometric coordinates for samples on a surface or of an environment.

2021年1月26日 · For 3D vision, the toolbox supports single, stereo, and fisheye camera calibration; stereo vision; 3D reconstruction; and lidar and 3D point cloud processing. Computer vision apps automate ground truth labeling and camera calibration workflows.

Implement Point Cloud SLAM in MATLAB. A point cloud is a set of points in 3-D space. Point clouds are typically obtained from 3-D scanners, such as a lidar or Kinect ® device. They have applications in robot navigation and perception, depth estimation, stereo vision, visual registration, and advanced driver assistance systems (ADAS).