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This paper introduces a novel framework for dynamic object detection and tracking using a quadruped robot equipped with a LiDAR sensor and an upward-facing fisheye camera. By fusing LiDAR data with RGB imagery, the method effectively identifies and labels moving objects in real-time, leveraging a cylindrical panorama projection to enhance the Kalman filter's observation updates. The system exhibits high precision and robustness, particularly in managing the transitions of objects between dynamic and static states, making it suitable for complex construction environments.
Real-time dynamic object tracking in construction environments is revolutionized by a robust sensor fusion model that combines LiDAR and fisheye imagery.
Robust dynamic object detection and tracking are essential for enabling robots to operate safely and effectively alongside humans in complex environments such as construction sites. While LiDAR-based SLAM and occupancy grid methods offer viable solutions for detecting and tracking motion, many state-of-the-art 3D vision approaches rely heavily on pre-trained neural networks and require additional post-processing to identify moving objects. Sensor fusion techniques, combining the precision of LiDAR with the semantic richness of RGB imagery, offer a promising alternative. In this work, we present a novel framework that enhances a quadruped robot equipped with a LiDAR sensor and an upward-facing fisheye camera for real-time dynamic object detection and tracking. After identifying moving objects within a registered point cloud, our method assigns semantic labels by projecting 3D coordinates onto a 2D cylindrical panorama, aligning with real-time image-based detections for observation update of the Kalman filter. The proposed system demonstrates high precision, simplicity, and robustness, particularly in handling objects transitioning between dynamic and static states, thus it is well-suited for deployment in real-world construction environments.
