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Robust stereo visual odometry: A comparison of random sample consensus algorithms based on three major hypothesis generators

Published online by Cambridge University Press:  12 May 2022

Guangzhi Guo ,
Zuoxiao Dai  and
Yuanfeng Dai
Guangzhi Guo
Affiliation:
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China
Zuoxiao Dai*
Affiliation:
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China
Yuanfeng Dai
Affiliation:
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China University of Chinese Academy of Sciences, Beijing, China
*
*Corresponding author. E-mail: tcgd_daizx@163.com
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Abstract

Almost all robust stereo visual odometry work uses the random sample consensus (RANSAC) algorithm for model estimation with the existence of noise and outliers. To date, there have been few comparative studies to evaluate the performance of RANSAC algorithms based on different hypothesis generators. In this work, we analyse and compare three popular and efficient RANSAC schemes. They mainly differ in using the two-dimensional (2-D) data points measured directly and the three-dimensional (3-D) data points inferred through triangulation. This comparison presents several quantitative experiments intended for comparing the accuracy, robustness and efficiency of each scheme under varying levels of noise and different percentages of outlier conditions. The results suggest that in the presence of noise and outliers, the perspective-three-point RANSAC provides more accurate and robust pose estimates. However, in the absence of noise, the iterative closest point RANSAC obtains better results regardless of the percentage of outliers. Efficiency, in terms of the number of RANSAC iterations, is found in that the relative speed of the perspective-three-point RANSAC becomes superior under low noise levels and low percentages of outlier conditions. Otherwise, the iterative closest-point RANSAC may be computationally more efficient.

Keywords

stereo visual odometryRANSAChypothesis generator
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 6 , November 2022 , pp. 1298 - 1309
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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