Enhancing Subsea Navigation Capabilities

增强海底导航能力

• 批准号: 518397-2017
• 负责人: Forbes, James
• 金额: $ 6.88万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650925
• 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.

该项目的重点是通过研究和开发新技术来提高自主水下航行器（AUV）姿态估计的最先进水平，以提高导航方法的可靠性和保真度。姿态估计是使用传感器数据估计车辆位置和姿态的过程。传统上，AUV的位姿估计是通过“航位推算”导航来实现的，这是一种依赖于多普勒速度测井（DVL）和惯性测量单元（IMU）数据的导航策略。由于 DVL 和 IMU 数据的固有偏差，航位推算姿态估计会随着时间的推移而出现“漂移”，这意味着姿态估计误差随时间线性增长。这个由 NSERC 赞助的项目的重点是使用 2G Robotics 的相机结合 DVL 和 IMU 数据来估计 AUV 的姿态。特别是，本科生、硕士生、博士生和博士后学生将研究 1) 如何使用视觉里程计正确估计和消除 DVL 和 IMU 数据中的偏差，2) 如何实现适合小型 AUV 的计算高效的位姿估计解决方案，以及3) 使用相机、DVL 和 IMU 数据实现同步定位和建图 (SLAM) 算法，以改进姿态估计和定位。这项研究的新颖之处在于使用了 2G Robotics 最先进的相机和照明技术，从而可以使用单目和立体相机，否则这些相机无法与 DVL 和 IMU 数据一起在水下环境中使用。作为这项研究的一部分，各级总部将获得在机器人平台上开发、实施和测试尖端导航算法的经验，2G Robotics 将获得相对于其他传感器提供商的竞争优势，以及加拿大在防盗领域的声誉。机器人将得到显着增强。