Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots

2025-10-28

AI Summary

In underwater SLAM, joint spatiotemporal calibration of Doppler Velocity Log (DVL) with other sensors (e.g., IMU, camera) remains challenging: existing methods fail to simultaneously estimate translational extrinsics and clock offset, and suffer from poor generalizability. To address this, we propose the Unified Iterative Calibration (UIC) framework. UIC is the first method to jointly estimate DVL translational extrinsics and time offset; it formulates a maximum a posteriori (MAP) optimization problem incorporating Gaussian process motion priors and introduces a statistically consistent sequential initialization strategy. The framework supports multi-configuration DVLs, integrates heterogeneous sensors (IMU, camera), and fuses high-precision motion interpolation with multimodal data. Extensive simulations and real-world underwater experiments demonstrate its high accuracy and robustness. We open-source a DVL–camera calibration toolbox, and the methodology generalizes to arbitrary multi-sensor systems.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Calibrating extrinsic parameters and clock offsets for underwater SLAM systems
• Addressing limitations in Doppler Velocity Log calibration methods
• Jointly estimating translational extrinsics and time offsets for sensors

Innovation

• Unified iterative calibration for general DVL setups
• Maximum a posteriori estimation with Gaussian process motion prior
• Statistically consistent sequential initialization scheme