Encoderless Gimbal Calibration of Dynamic Multi-Camera Clusters
This addresses the calibration challenge for dynamic camera clusters in robotics and vision applications, offering a practical solution for off-the-shelf mechanisms, though it is incremental as it builds on existing VIO frameworks.
The paper tackled the problem of calibrating dynamic multi-camera systems without relying on motor encoders, by developing an encoderless method that estimates kinematic parameters and joint angles, achieving localization accuracy comparable to static setups in experiments.
Dynamic Camera Clusters (DCCs) are multi-camera systems where one or more cameras are mounted on actuated mechanisms such as a gimbal. Existing methods for DCC calibration rely on joint angle measurements to resolve the time-varying transformation between the dynamic and static camera. This information is usually provided by motor encoders, however, joint angle measurements are not always readily available on off-the-shelf mechanisms. In this paper, we present an encoderless approach for DCC calibration which simultaneously estimates the kinematic parameters of the transformation chain as well as the unknown joint angles. We also demonstrate the integration of an encoderless gimbal mechanism with a state-of-the art VIO algorithm, and show the extensions required in order to perform simultaneous online estimation of the joint angles and vehicle localization state. The proposed calibration approach is validated both in simulation and on a physical DCC composed of a 2-DOF gimbal mounted on a UAV. Finally, we show the experimental results of the calibrated mechanism integrated into the OKVIS VIO package, and demonstrate successful online joint angle estimation while maintaining localization accuracy that is comparable to a standard static multi-camera configuration.