Position Based Camera-2D LiDAR Fusion and Person Following for Mobile Robots.

Person following is a crucial feature for mobile robots, with many using 2D LiDAR-based tracking due to its cost-effectiveness. However, such methods have limitations, such as tracking only from the front or back and being prone to false positives from environmental artifacts. More advanced tracking approaches combine position and appearance information. These can be classified into two categories: 1) position-based tracking, which fuses features from RGBD cameras and LiDARs, and 2) image-based tracking, which uses deep learning to match person appearances in images and then computes position via 3D de-projection. While position-based tracking has shown to be effective for short-term tracking, it has not been tested for person following application on a real robot. Image-based methods have been deployed on real robots but have not been compared to position-based tracking of target person for person following application, where consistent tracking without ID switching is crucial. This work presents a position-based target person tracking system tested on a real robot for person following application using deep learning for person detection, multi-sensor fusion (RGBD cameras and LiDAR), and UCMCtrack algorithm. We compare this with the SORT algorithms for image-based tracking. Our results show that position-based tracking is more suitable for person following, as image-based methods are prone to ID switching due to close proximity in images but distant in 3D space.