Trajectory-Based Visual Localization in Underwater Surveying Missions

Sensors

Antoni Burguera¹  Francisco Bonin-Font² 

[1] System, Robotics and Vision Group, Department Matemàtiques i Informàtica,Universitat de les Illes Balears, Ctra. Valldemossa, Km. 7.5, 07122 Palma de Mallorca, Spain;

关键词: underwater robotics;    visual localization;    data association;    image registration;   

DOI  :  10.3390/s150101708

来源: mdpi

【 摘 要 】

We present a new vision-based localization system applied to an autonomous underwater vehicle (AUV) with limited sensing and computation capabilities. The traditional EKF-SLAM approaches are usually expensive in terms of execution time; the approach presented in this paper strengthens this method by adopting a trajectory-based schema that reduces the computational requirements. The pose of the vehicle is estimated using an extended Kalman filter (EKF), which predicts the vehicle motion by means of a visual odometer and corrects these predictions using the data associations (loop closures) between the current frame and the previous ones. One of the most important steps in this procedure is the image registration method, as it reinforces the data association and, thus, makes it possible to close loops reliably. Since the use of standard EKFs entail linearization errors that can distort the vehicle pose estimations, the approach has also been tested using an iterated Kalman filter (IEKF). Experiments have been conducted using a real underwater vehicle in controlled scenarios and in shallow sea waters, showing an excellent performance with very small errors, both in the vehicle pose and in the overall trajectory estimates.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.