Structure-aware sample consensus for robust stereo visual odometry

Chun Liu, Zhengning Li, Zhuo Chen and Hangbin Wu

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 10, 1550147717736655

Abstract: With the expansion of underground infrastructure in the urban areas, positioning in such scenario becomes a crucial problem for the ubiquitous urban positioning applications. The visual odometry algorithm can provide an accurate position reference for ground applications. However, it encounters robust estimation problems in the underground because automatic feature matching from the underground structures is difficult and errors are quite frequent. In this article, we present a novel structure-aware sample consensus algorithm to solve the robust estimation problem in stereo visual odometry. Features from the rigid structure provide a static reference and are more likely to be inliers, based on which we introduce a structure feature-guided sampling procedure instead of the random sampling procedure as used in random sample consensus. With this novel procedure, the structure-aware sample consensus gains more possibility to generate a correct motion model and performs as a robust estimator for the underground visual odometry algorithm. The experiments with both synthetic and real-world data show that structure-aware sample consensus outperforms the random sample consensus and its variants in robustness, while maintaining a lower computational cost. In addition, the structure-aware sample consensus–based visual odometry algorithm maintains the same performance level of robustness and accuracy for both ground and underground scenarios, which makes the algorithm applicable for ubiquitous urban positioning systems.

Keywords: Visual odometry; underground positioning; robust estimation; random sample consensus; underground dataset (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:10:p:1550147717736655

DOI: 10.1177/1550147717736655

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