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http://dx.doi.org/10.3837/tiis.2017.01.016

Robust Features and Accurate Inliers Detection Framework: Application to Stereo Ego-motion Estimation  

MIN, Haigen (College of Information Engineering, University of Chang'an)
ZHAO, Xiangmo (College of Information Engineering, University of Chang'an)
XU, Zhigang (College of Information Engineering, University of Chang'an)
ZHANG, Licheng (College of Information Engineering, University of Chang'an)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.1, 2017 , pp. 302-320 More about this Journal
Abstract
In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.
Keywords
local invariant feature; AKAZE; Ego-motion estimation; RANSAC; Iterated sigma point Kalman Filter;
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