  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
