统一标定水下机器人多传感器的位姿与时间偏移，提升定位精度。
Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots
- 基于高斯过程先验和最大后验估计，迭代优化运动状态与标定参数。
- 首次联合估计平移外参与时间偏移，适用于多种传感器组合。
- 开源工具箱支持实测验证，方法可推广至其他多传感器系统。
水下SLAM系统中，传感器外参与时钟偏移的精确标定仍缺乏充分研究。现有多普勒速度计（DVL）标定方法受限于特定传感器配置或依赖过度简化的假设，且均未联合估计平移外参与时间偏移。本文提出一种通用的统一迭代标定（UIC）框架，以最大后验（MAP）估计为基础，引入高斯过程（GP）运动先验实现高保真运动插值。UIC交替进行高效的基于GP的运动状态更新与梯度驱动的标定变量更新，并配备可证明统计一致性的顺序初始化方案。该方法可扩展至惯性测量单元（IMU）、相机等其他模态作为共传感器。我们发布了开源的DVL-相机标定工具箱。除水下应用外，UIC中融合GP先验的MAP标定机制及可证明可靠的初始化设计，对其他多传感器标定问题亦具广泛适用性。仿真与实测验证了所提方法的有效性。
原文摘要 · Abstract (English)
The calibration of extrinsic parameters and clock offsets between sensors for high-accuracy performance in underwater SLAM systems remains insufficiently explored. Existing methods for Doppler Velocity Log (DVL) calibration are either constrained to specific sensor configurations or rely on oversimplified assumptions, and none jointly estimate translational extrinsics and time offsets. We propose a Unified Iterative Calibration (UIC) framework for general DVL sensor setups, formulated as a Maximum A Posteriori (MAP) estimation with a Gaussian Process (GP) motion prior for high-fidelity motion interpolation. UIC alternates between efficient GP-based motion state updates and gradient-based calibration variable updates, supported by a provably statistically consistent sequential initialization scheme. The proposed UIC can be applied to IMU, cameras and other modalities as co-sensors. We release an open-source DVL-camera calibration toolbox. Beyond underwater applications, several aspects of UIC-such as the integration of GP priors for MAP-based calibration and the design of provably reliable initialization procedures-are broadly applicable to other multi-sensor calibration problems. Finally, simulations and real-world tests validate our approach.
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