Enhancing Subsea Navigation Capabilities

增强海底导航能力

• 批准号: 518397-2017
• 负责人: Forbes, James
• 金额: $ 3.7万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=719473
• 关键词: Enhancing; Subsea; Navigation; Capabilities

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.

该项目致力于通过研究和开发新技术来提高自动水下汽车（AUV）的最新姿势，以提高可靠性和忠实导航方法。姿势估计是使用传感器数据估算车辆位置和态度的过程。传统上，AUV的姿势估计是通过“死刑”导航实现的，这是一种依赖多普勒速度日志（DVL）和惯性测量单元（IMU）数据的导航策略。由于两个DVL和IMU数据中的固有偏见，随着时间的推移，dead-reckoning姿势估计均遭受“漂移”的损失，这意味着姿势估计误差随时间线性增长。该NSERC赞助项目的重点是与DVL和IMU数据结合使用2G机器人的摄像机来估计AUV的姿势。特别是，本科，硕士，博士和博士后学生将调查1）如何使用视觉探调仪正确估计和否定DVL和IMU数据中的偏见，2）如何实现适合较小AUV的计算高效姿势估计解决方案，以及3）实现同时的本地化和映射（SLAM），并使用相机（SLAM）algith，DV，DV，IM IMP，DV，DV本地化。拟议的研究的新颖性在于使用2G机器人技术和照明技术的使用，从而实现了单眼和立体声摄像机的使用，否则，与DVL和IMU数据结合使用，否则就无法在水下环境中使用。作为这项研究的一部分，所有级别的HQP将获得在机器人平台上开发，实施和测试尖端导航算法方面的经验，相对于其他传感器提供商，2G机器人技术将获得竞争优势，并且加拿大在机器人Burgling领域的声誉将得到显着增强。