Targetless and Bias-Aware Extrinsic Calibration of Underwater Vehicles

Abstract

Inspection-class autonomous underwater vehicles often operate without external positioning. Instead, they use onboard sensors to estimate their state. Because no single sensor is sufficient in isolation, they typically combine a camera, an inertial measurement unit (IMU), a Doppler velocity log (DVL), and a pressure sensor, fused within a simultaneous localization and mapping (SLAM) estimator. Coherent fusion requires extrinsic calibration so that all measurements are expressed in a common frame. Existing calibration methods address only part of the problem: they often calibrate subsets of sensors, require dedicated calibration procedures or targets, adopt continuous-time formulations not directly compatible with a discrete-time estimator, or neglect the impact of uncertainty in the motion quantities used during calibration. This thesis develops a targetless joint extrinsic calibration that recovers the full set of extrinsic parameters between the camera, IMU, DVL, and pressure sensor from onboard data. The method is formulated within a discrete-time SLAM estimator. To the best of our knowledge, no prior method estimates the pressure-sensor translation jointly with the rest of the suite. First, we show that the established baseline carries a structural bias in the IMU--DVL translation, present regardless of data quality. Our corrected formulation removes this bias, establishing a precondition for any joint calibration built on the baseline. Second, we identify a subtle bias that remains, an errors-in-variables effect from conditioning on a noisy motion measurement. This bias cannot be eliminated through tuning. We show that its severity can be predicted from the sensor data before the run, without ground truth. A further reformulation reduces it. Finally, evaluated on real underwater data, the calibration runs on demand during a mission and improves the IMU--DVL translation estimate over the baseline, in the order predicted by the analysis.