Tightly-Coupled Visual- DVL- Inertial Odometry for Robot-Based Ice-Water Boundary Exploration
Lin Zhao, Mingxi Zhou, Brice Loose
Abstract
Underwater robots, like Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for the exploration and study of the under-ice environment and the ecosystems that thrive there. However, state estimation is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper, wwe present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated using a Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides, a modified keyframe-based clone marginalization and a new DVL-aided feature enhancement method are presented to further improve the localization performance. The proposed method is validated in the under-ice environment on Lake Michigan, USA, and the results are cross-compared with 10 other different sensor fusion setups. Overall, the integration of keyframe enabled and DVL-aided feature enhancement yielded the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path over a total traveling distance of about 200 m.
BibTeX
@inproceedings{iros2023_tightlycoupledvi,
title = {Tightly-Coupled Visual- DVL- Inertial Odometry for Robot-Based Ice-Water Boundary Exploration},
author = {Lin Zhao and Mingxi Zhou and Brice Loose},
booktitle = {IROS 2023},
year = {2023}
}