라이다 추적

라이다를 사용하여 확장 객체 추적

아래의 예제에서는 trackerJPDA 및 trackerGridRFS와 같은 라이다 탐지와 다중 객체 추적기를 사용하여 확장 객체를 추적합니다.

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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.

R2024b 이후
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Detect, Classify, and Track Vehicles Using Lidar

Detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle. The lidar data used in this example is recorded from a highway-driving scenario. In this example, the point cloud data is segmented to determine the class of objects using the PointSeg network. A joint probabilistic data association (JPDA) tracker with an interactive multiple model filter is used to track the detected vehicles.

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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.

스크립트 열기

Object-Level Fusion of Lidar and Camera Data for Vehicle Tracking

Fuse lidar and camera data to track vehicles using a JIPDA tracker.

R2023a 이후
라이브 스크립트 열기

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.

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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.

R2023a 이후
라이브 스크립트 열기

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.

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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.

R2021b 이후
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