Authors:
InJun Mun
and
Sukhan Lee
Affiliation:
Intelligent Systems Research Institute, Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, South Korea
Keyword(s):
Localization, Visual Odometry, Essential Matrix, Optimal Feature Selection, Uncertainty Hypervolume, Bucketing.
Abstract:
Visual odometry based on point feature matching has been well-established. Notably, methods based on essential and fundamental as well as homography matrices have been widely used. It is known that the accuracy of visual odometry is affected by the choice of matched feature point pairs. However, no mathematically rigorous formula relating the choice of feature point pairs to the uncertainty involved in visual odometry is available. Instead, point selection heuristics based on feature point distribution combined with RANSAC-based refinement are mostly adopted to ensure accuracy. In this paper, we present “Uncertainty Hypervolume” as a rigorous mathematical formula that relates the selected feature point pairs to the uncertainty of visual odometry. The uncertainty hypervolume associated with selected feature point pairs provides a precise metric for evaluating the selected feature point pairs and the resulting visual odometry. This metric is useful in practice not only for selecting th
e best feature point pairs but also for selecting poor feature point pairs available for visual odometry. Furthermore, it accurately identifies the uncertainty in visual odometry, which helps better manage the performance of visual odometry applications.
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