Automatic calibration of laser range finder positions for pedestrian tracking based on social group detections

摘要

Laser range finders (LRF's) are non-invasive sensors which can be used for high-precision and anonymous tracking of pedestrians in social environments. Such sensor networks can be used in robotics to assist in navigation and human-robot interaction. Typically, multiple LRF's are used together for such tasks, and the relative positions of these sensors must be precisely calibrated. We propose a technique for estimating relative LRF positions using observations of social groups in the pedestrian flow as keypoint features for determining coarse estimates of relative sensor offsets. The most likely offset is estimated using a generalized Hough transform and used to identify sets of possible shared observations of individual pedestrians between pairs of sensors. Outliers are rejected using the RANSAC technique, and the resulting shared observations from each sensor pair are combined into a constraint matrix for the sensor network, which is solved using least-squares minimization. Results show calibration accuracy of sensor positions within 34 mm and 0.51°, and an analysis of pedestrian data collected from ubiquitous networks in three public and commercial spaces shows that the proposed calibration technique enables pedestrian tracking within 11 cm accuracy.