Evaluation of Combined Time-Offset Estimation and Hand-Eye Calibration on Robotic Datasets

Abstract

Using multiple sensors often requires the knowledge of static transformations between those sensors. If these transformations are unknown, hand-eye calibration is used to obtain them. Additionally, sensors are often unsynchronized, thus requiring time-alignment of measurements. This alignment can further be hindered by having sensors that fail at providing useful data over a certain time period. We present an end-to-end calibration framework to solve the hand-eye calibration. After an initial time-alignment step, we use the time-aligned pose estimates to perform the static transformation estimation based on different prefiltering methods, which are robust to outliers. In a final step, we employ a non-linear optimization to locally refine the calibration and time-alignment. Successful application of this estimation framework is demonstrated on multiple robotic systems with different sensor configurations. This framework is released as open source software together with the datasets.