Active Planning Based Extrinsic Calibration Of Exteroceptive Sensors In Unknown Environments

Existing Simultaneous Localization and Mapping systems require an extensive manual pre-calibration process. Non-manual calibration procedures use manipulators to create known patterns in order to estimate the unknown calibration. Calibration is often time-consuming and involves humans performing repetitive tasks such as aligning a known calibration target at different poses with respect to the sensor. We propose an algorithm that plans a trajectory which actively reduces the uncertainty of the robot's calibration given a rough initial calibration estimate. Calibration is performed autonomously in a previously unknown environment by maintaining the belief over landmarks, poses, and the calibration parameters. We present experimental results to demonstrate the approach's ability to autonomously calibrate the exteroceptive sensor in simulated and real environments. We show that even a greedy approach can reduce the effort needed to perform calibration every time the robot is reconfigured for autonomous tasks and mitigates the possibility of human error added into the calibration.