Mobile robots should possess accurate self-localization capabilities in order to be successfully deployed in their environment. A solution to this challenge may be derived from visual odometry (VO), which is responsible for estimating the robot's pose by analysing a sequence of images. The present paper proposes an accurate, computationally-efficient VO algorithm relying solely on stereo vision images as inputs. The contribution of this work is twofold. Firstly, it suggests a non-iterative outlier detection technique capable of efficiently discarding the outliers of matched features. Secondly, it introduces a hierarchical motion estimation approach that produces refinements to the global position and orientation for each successive step. Moreover, for each subordinate module of the proposed VO algorithm, custom non-iterative solutions have been adopted. The accuracy of the proposed system has been evaluated and compared with competent VO methods along DGPS-assessed benchmark routes. Experimental results of relevance to rough terrain routes, including both simulated and real outdoors data, exhibit remarkable accuracy, with positioning errors lower than 2%.
Stereo-Based Visual Odometry for Autonomous Robot Navigation
2016-02-10
Kostavelis , I , Boukas , E , Nalpantidis , L & Gasteratos , A 2016 , ' Stereo-Based Visual Odometry for Autonomous Robot Navigation ' , International Journal of Advanced Robotic Systems , vol. 13 , no. 1 , 21 . https://doi.org/10.5772/62099
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
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