Automatic extrinsic calibration of depth sensors with ambiguous environments and restricted motion

Abstract

Autonomous mobile robots that use multiple depth sensors to perceive their environments, rely on extrinsic calibration to combine the individual views from each sensor into a single coherent view of the surroundings. Such extrinsic calibration is tedious to perform manually, and requires that specific scenes to calibrate. Current state of the art automatic approaches do not consider the content of scenes used for calibration, and thus are not robust to partially informative scenes in long-term deployments. In this paper, we present Delta-Calibration, an automated extrinsic calibration technique that takes into account the information in a scene for calibration. Delta-Calibration relies on constrained sensor motion to minimize the effects of desynchronization, and ego-motion estimation from each depth camera to detect significant changes in pose, which we term Delta-Transforms. We derive a solution to the extrinsic calibration using such Delta-Transforms taking into account uncertain axes of motion in the environment, and further infer necessary and sufficient conditions on the Delta-Transforms such that Delta-Calibration results in a unique, non-singular, and numerically stable extrinsic calibration. We present quantitative and qualitative results demonstrating the effectiveness of Delta-Calibration at computing extrinsic calibration over different arrangements of depth sensors.