Enhancing Subsea Navigation Capabilities

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This project focuses on advancing the state-of-the-art of autonomous underwater vehicle (AUV) pose estimation through research and development of new techniques to increase the reliability and fidelity navigation methodologies. Pose estimation is the process of estimating position and attitude of a vehicle using sensor data. Traditionally, pose estimation of an AUV is realized through "dead-reckoning" navigation, a navigation strategy that relies on doppler velocity log (DVL) and inertial measurement unit (IMU) data. Due to inherent bias in both DVL an IMU data, dead-reckoning pose estimation suffered from "drift" over time, meaning the pose estimation error grows linearly with time. The focus of this NSERC sponsored project is to use 2G Robotics' cameras in conjunction with DVL and IMU data to estimate the pose of an AUV. In particular, undergraduate, master's, PhD, and postdoctoral students will investigate 1) how to use visual odometry to correctly estimate and negate bias in DVL and IMU data, 2) how to realize a computationally efficient pose estimation solution suitable for smaller AUVs, and 3) implement a simultaneous localization and mapping (SLAM) algorithm using camera, DVL, and IMU data to improve both pose estimation and localization. The novelty of the proposed research lies in the use of 2G Robotics state of the art camera and lighting technology that enables the use of monocular and stereo cameras that, otherwise, cannot be used in the underwater environment in conjunction with DVL and IMU data. As part of this research, HQP at all levels will gain experience in developing, implementing, and testing cutting-edge navigation algorithms on robotic platforms, 2G Robotics will gain a competitive advantage relative to other sensor providers, and Canada's reputation in the burgling field of robots will be significantly enhanced.