Observability-Aware Active Extrinsic Calibration of Multiple Sensors

Shida Xu, Jonatan Scharff Willners, Ziyang Hong, Kaicheng Zhang, Yvan R. Petillot, Sen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The extrinsic parameters play a crucial role in multi-sensor fusion, such as visual-inertial Simultaneous Localization and Mapping(SLAM), as they enable the accurate alignment and integration of measurements from different sensors. However, extrinsic calibration is challenging in scenarios, such as underwater, where in-view structures are scanty and visibility is limited, causing incorrect extrinsic calibration due to insufficient motion on all degrees of freedom. In this paper, we propose an entropy-based active extrinsic calibration algorithm leverages observability analysis and information entropy to enhance the accuracy and reliability of extrinsic calibration. It determines the system observability numerically by using singular value decomposition (SVD) of the Fisher Information Matrix (FIM). Furthermore, when the extrinsic parameter is not fully observable, our method actively searches for the next best motion to recover the system's observability via entropy-based optimization. Experimental results on synthetic data, in a simulation, and using an actual underwater vehicle verify that the proposed method is able to avoid the calibration failure while improving the calibration accuracy and reliability.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Robotics and Automation (ICRA)
Number of pages7
ISBN (Electronic)9798350323658
Publication statusPublished - 4 Jul 2023
Event2023 IEEE International Conference on Robotics and Automation - London, United Kingdom
Duration: 29 May 20232 Jun 2023


Conference2023 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2023
Country/TerritoryUnited Kingdom


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