Calibrating Depth Sensors for Pedestrian Tracking Using a Robot as a Movable and Localized Landmark

Using multiple depth sensors enables us to accurately track pedestrians in real environments. Accurate pedestrian positions are essential for building an effective human-centered cyber world provided by location-based services. In particular, ceiling-mounted depth sensors can robustly track people in such environments. However, one important problem for this approach is the accurate calibration of the absolute sensor positions. This problem remains unsolved due to limited range and sensor distortions from a distance. Manual calibration is complicated and time-consuming, and the existing calibration method still has several limitations since it used a pedestrian as a movable landmark. Instead of a human landmark, we propose a method that uses a mobile robot as a movable and localized landmark to calibrate each sensor. We compared the calibration performance of the proposed and existing methods and showed that the former achieved more accurate calibration for both the absolute sensor and tracked pedestrian positions. Our proposed method with a mobile robot not only increased the accuracy of the calibration processes but also decreased human efforts.