Robotic vehicles working in unknown environments require the ability to determine their location while learning about obstacles located around them. A classic approach to solving this problem is feature-based Simultaneous Localization and Mapping (SLAM) using an Extended Kalman Filter (EKF). In this paper, feature-based EKF SLAM is used to examine the feasibility of using a low cost vision-based sensor for performing SLAM in enclosed underwater environments. Classic acoustic-based range sensors suffer from poor performance in enclosed areas due to reflections, furthermore the relatively high cost of acoustic-based navigation sensors prevents their use on low cost underwater vehicles. To overcome these challenges, a custom vision-based range finder and a downward facing camera for the implementation of a standard feature tracking algorithm are used to perform EKF SLAM.
- Dynamic Systems and Control Division
Testing Vision-Based Sensors for Enclosed Underwater Environments When Applied to EKF SLAM
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Cain, CH, & Leonessa, A. "Testing Vision-Based Sensors for Enclosed Underwater Environments When Applied to EKF SLAM." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 213-220. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8747
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