Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications

Sensors

Thomas Kriechbaumer¹  Kim Blackburn²  Toby P. Breckon³  Oliver Hamilton³  Monica Rivas Casado¹  João Valente⁴ 

[1] School of Energy, Environmental Technology and Agrifood, Cranfield University, Cranfield MK43 0AL, UK;School of Aerospace, Transport Systems and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK;School of Engineering and Computing Sciences, Durham University, Durham DH1 3LE, UK;;School of Energy, Environmental Technology and Agrifood, Cranfield University, Cranfield MK43 0AL, UK

关键词: visual odometry;    river monitoring;    stereo vision;    autonomous watercraft;    survey vessel;    autonomous river navigation;    GPS-denied environments;   

DOI  :  10.3390/s151229892

来源: mdpi

【 摘 要 】

Autonomous survey vessels can increase the efficiency and availability of wide-area river environment surveying as a tool for environment protection and conservation. A key challenge is the accurate localisation of the vessel, where bank-side vegetation or urban settlement preclude the conventional use of line-of-sight global navigation satellite systems (GNSS). In this paper, we evaluate unaided visual odometry, via an on-board stereo camera rig attached to the survey vessel, as a novel, low-cost localisation strategy. Feature-based and appearance-based visual odometry algorithms are implemented on a six degrees of freedom platform operating under guided motion, but stochastic variation in yaw, pitch and roll. Evaluation is based on a 663 m-long trajectory (>15,000 image frames) and statistical error analysis against ground truth position from a target tracking tachymeter integrating electronic distance and angular measurements. The position error of the feature-based technique (mean of ±0.067 m) is three times smaller than that of the appearance-based algorithm. From multi-variable statistical regression, we are able to attribute this error to the depth of tracked features from the camera in the scene and variations in platform yaw. Our findings inform effective strategies to enhance stereo visual localisation for the specific application of river monitoring.

【 授权许可】

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