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LiDAR 追跡
これらの例では、LiDAR 検出と、trackerJPDA や trackerGridRFS などのマルチオブジェクト トラッカーを使用して、拡張オブジェクトを追跡します。
注目の例
Highway Vehicle Tracking Using Multi-Sensor Data Fusion
Track vehicles on a highway with commonly used sensors such as radar, camera, and lidar. In this example, you configure and run a Joint Integrated Probabilistic Data Association (JIPDA) tracker to track vehicles using recorded data from a suburban highway driving scenario.
LiDAR を使用した車両の検出、分類、および追跡
この例では、エゴ ビークルに取り付けられている LiDAR センサーによって取得された LiDAR 点群データを使用して、車両を検出、分類、追跡する方法を説明します。この例で使用する LiDAR データは、WPI Lidar Visual SLAM Dataset [1] に含まれているハイウェイ走行シナリオから記録されています。この例では、PointSeg ネットワークを使用して、オブジェクトのクラスを特定するために点群データをセグメント化します。Interactive Multiple Model フィルターを含む Joint Probabilistic Data Association (JPDA) トラッカーを使用して、検出された車両を追跡します。
Track Vehicles Using Lidar: From Point Cloud to Track List
Track vehicles using measurements from a lidar sensor mounted on top of an ego vehicle.
Object-Level Fusion of Lidar and Camera Data for Vehicle Tracking
Fuse lidar and camera data to track vehicles using a JIPDA tracker.
Grid-Based Tracking in Urban Environments Using Multiple Lidars
Track moving objects with multiple lidars using a grid-based tracker. A grid-based tracker enables early fusion of data from high-resolution sensors such as radars and lidars to create a global object list.
Fuse Prerecorded Lidar and Camera Data to Generate Vehicle Track List for Scenario Generation
Fuse prerecorded lidar and camera object detections to create a smoothed vehicle track list.
Track Vehicles Using Lidar Data in Simulink
Track vehicles using measurements from a lidar sensor mounted on top of an ego vehicle. Due to high resolution capabilities of the lidar sensor, each scan from the sensor contains a large number of points, commonly known as a point cloud. The example illustrates the workflow in Simulink for processing the point cloud and tracking the objects. The lidar data used in this example is recorded from a highway driving scenario. You use the recorded data to track vehicles with a joint probabilistic data association (JPDA) tracker and an interacting multiple model (IMM) approach. The example closely follows the Track Vehicles Using Lidar: From Point Cloud to Track List MATLAB® example.
Grid-based Tracking in Urban Environments Using Multiple Lidars in Simulink
Track moving objects with multiple lidars using a grid-based tracker in Simulink. You use the Grid-Based Multi Object Tracker Simulink block to define the grid-based tracker. This Grid-based tracker uses dynamic occupancy grid map as an intermediate representation of the environment. This example closely follows the Grid-Based Tracking in Urban Environments Using Multiple Lidars MATLAB® example.
