Spatiotemporal Calibration of Doppler Velocity Logs for Underwater Robots

TL;DR

This paper introduces a unified iterative framework for calibrating Doppler Velocity Logs in underwater robots, jointly estimating sensor extrinsics and clock offsets with high accuracy using Gaussian Process priors.

Contribution

The paper presents a novel MAP-based calibration method that jointly estimates extrinsic parameters and time offsets, applicable to various sensor configurations including DVL, IMU, and cameras.

Findings

Effective joint calibration of DVL and other sensors demonstrated in simulations.

The proposed method outperforms existing calibration approaches in accuracy.

Open-source toolbox facilitates practical adoption and testing.

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.