2D Laser and 3D Camera Data Integration and Filtering for Human Trajectory Tracking

This paper addresses a robust human trajectory tracking method through the data integration of 2D laser scanner and 3D camera. Mapping the deep learning-based 3D camera human detection onto the point cloud of the depth information to build up the 3D bounding box-represented human and using the state-of-the-art 2D laser-based leg detection are the main data streams of the human tracking system. The human-oriented global nearest neighbour (HOGNN) data association, inspired from the Hall’s proxemics, was developed to improve both the 3D camera-based and 2D laser-based human detection techniques. The dual Kalman filters are employed to track the human trajectory in parallel. The integration of the 3D camera-based and 2D laser-based human tracking is the key function of the system providing the real-time feedback for both the HOGNN to reduce false-positives of the camera-based and laser-based human detection and the Kalman filter to enhance the quality of the human trajectory tracking under uncertain environmental conditions. We implemented the sensor integration on ROS and validated it through real-world experiments.