SNAPCAT-3D: Calibrating networks of 3D range sensors for pedestrian tracking

Abstract

The use of 3D range sensors for human position tracking has grown in recent years, especially for augmenting robotic sensing for human-robot interaction. However, extrinsic calibration of the relative positions of 3D range sensors is difficult, due to their limited range, narrow field of view, and distortion at large distances. 2D laser range finders have also been used for pedestrian tracking, providing greater accuracy and coverage at the cost of being more expensive and susceptible to occlusion. In this work, we present two novel techniques for calibrating the positions of 3D range sensors based on shared observations of pedestrians. The first technique uses 3D range sensors alone, and the second technique uses 2D and 3D range sensors together, using the high precision and long range of the 2D sensors to complement the short-range but richer sensing of 3D range sensors. We evaluate the accuracy of both automatic calibration techniques, and we furthermore show that the combination of 2D and 3D sensors gives more robust and accurate calibration than when using 3D sensors alone.