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

翻译：在动态环境中，实时检测与跟踪运动目标的能力对于自主机器人实现安全高效导航至关重要。传统的动态目标检测方法依赖高精度里程计与地图来检测和跟踪运动目标。然而，在周围环境持续变化的动态环境中，这些方法并不适用于长期运行。为解决此问题，我们提出了一种仅使用LiDAR数据实时检测与跟踪动态目标的新系统。通过强调从LiDAR数据中提取低频分量作为前景目标的特征点，我们的方法显著减少了目标聚类与运动分析所需的时间。此外，我们开发了一种跟踪方法，该方法采用基于强度的自运动估计结合滑动窗口技术来评估目标运动。这使得系统能够精确识别运动目标，并增强其对里程计漂移的鲁棒性。实验表明，该系统能以88.7%的平均检测精度和89.1%的召回率实时检测与跟踪动态目标。此外，我们的系统对通常仅与前端LiDAR里程计相关的长期漂移表现出鲁棒性。所有源代码、标注数据集及标注工具均公开于：https://github.com/MISTLab/lidar_dynamic_objects_detection.git