Simultaneous Estimation of Euclidean Distances to a Stationary Object's Features and the Euclidean Trajectory of a Monocular Camera

• 成果类型: Article
• 署名作者: Bell, Zachary, I; Deptula, Patryk; Doucette, Emily A.; Curtis, J. Willard; Dixon, Warren E.
• 署名单位: State University System of Florida; University of Florida
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3035597
• 发表日期: 2021
• 页码: 4252-4258
• 关键词: Cameras; observers; Feature extraction; CONVERGENCE; trajectory; Global Positioning System; Angular velocity; computer vision; nonlinear observers; simultaneous localization and mapping (SLAM); structure from motion (SfM); vision-based localization

摘要

Data-based, exponentially converging observers are developed for a monocular camera to estimate the Euclidean distance (and hence accurately scaled coordinates) to features on a stationary object and to estimate the Euclidean trajectory taken by the camera while tracking the object, without requiring the typical positive depth constraint. A Lyapunov-based stability analysis shows that the developed observers are exponentially converging without requiring persistence of excitation through the use of a data-based learning method. An experimental study is presented, which compares the developed Euclidean distance observer to previous observers demonstrating the effectiveness of this result.