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.
CITATION STYLE
Furrer, F., Fehr, M., Novkovic, T., Sommer, H., Gilitschenski, I., & Siegwart, R. (2018). Evaluation of Combined Time-Offset Estimation and Hand-Eye Calibration on Robotic Datasets. In Springer Proceedings in Advanced Robotics (Vol. 5, pp. 145–159). Springer Science and Business Media B.V. https://doi.org/10.1007/978-3-319-67361-5_10
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