LiDAR-based Real-Time Object Detection and Tracking in Dynamic Environments

TL;DR

This paper presents a novel LiDAR-based system for real-time detection and tracking of moving objects in dynamic environments, emphasizing low-frequency feature extraction and intensity-based ego-motion estimation to improve accuracy and resilience.

Contribution

The authors introduce a new LiDAR-only method that reduces processing time and enhances robustness for real-time dynamic object detection and tracking in changing environments.

Findings

Achieved 88.7% detection accuracy and 89.1% recall rate.

Demonstrated resilience to odometry drift in dynamic scenarios.

Provided open-source code, dataset, and annotation tools.

Abstract

In dynamic environments, the ability to detect and track moving objects in real-time is crucial for autonomous robots to navigate safely and effectively. Traditional methods for dynamic object detection rely on high accuracy odometry and maps to detect and track moving objects. However, these methods are not suitable for long-term operation in dynamic environments where the surrounding environment is constantly changing. In order to solve this problem, we propose a novel system for detecting and tracking dynamic objects in real-time using only LiDAR data. By emphasizing the extraction of low-frequency components from LiDAR data as feature points for foreground objects, our method significantly reduces the time required for object clustering and movement analysis. Additionally, we have developed a tracking approach that employs intensity-based ego-motion estimation along with a sliding window technique to assess object movements. This enables the precise identification of moving objects and enhances the system's resilience to odometry drift. Our experiments show that this system can detect and track dynamic objects in real-time with an average detection accuracy of 88.7\% and a recall rate of 89.1\%. Furthermore, our system demonstrates resilience against the prolonged drift typically associated with front-end only LiDAR odometry. All of the source code, labeled dataset, and the annotation tool are available at: https://github.com/MISTLab/lidar_dynamic_objects_detection.git