Abstract
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%.
Highlights
Mobile robots require highly sophisticated and accurate algorithms to achieve accurate location estimations while moving in unknown environments and possessing no prior knowledge of the scene or the robot’s motion [1]
Visual odometry (VO) algorithms are able to provide a solution in this respect, and the development of such effective algo‐ rithms has become an active research topic as a result
Compared to standard wheel odometry, VO encapsulates a number of advantages, such as the elimination of errors due to wheel slippage in estimating the camera position and the ability to obtain 3D motion estimations even with non-planar surfaces
Summary
Mobile robots require highly sophisticated and accurate algorithms to achieve accurate location estimations while moving in unknown environments and possessing no prior knowledge of the scene or the robot’s motion [1]. VO has become a prerequisite for many practical requirements of robotics technology, such as obstacle avoidance, simultaneous localization and mapping, and even in path-planning It is only in applications where external disturbances of the camera do not allow for a VO solution that researchers investigate other odometry solutions, such as inertial-based solutions [2, 3]. The step consists of a non-iterative outlier detection methodology able to discard both the mismatches between the features and the inserted errors due to the 3D reconstruction procedure. The presentation of a hierarchical motion estimation method producing robust orientation and position estimations while providing refinements to the robot’s trajectory at every single step
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