Lidar Toolbox 시작하기

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

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.

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

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

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.

이 예제에서는 항공 라이다 데이터에 대해 의미론적 분할을 수행하도록 PointNet++ 딥러닝 신경망을 훈련시키는 방법을 보여줍니다.

Train a PointPillars network for object detection in point clouds.

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.