International Journal of Robotics Research
1-point RANSAC for EKF-based structure from motion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Jointly compatible pair linking for visual tracking with probabilistic priors
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximum-likelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a sampling-based algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. Our work is motivated by real-time tracking and the estimation of structure from motion (SFM). We test our algorithm for on-line outlier rejection both for tracking and for SFM. We show that our approach can tolerate a large proportion of outliers, whereas previous causal robust statistical inference methods failed with less than half as many. Our work can be thought of as the extension of random sample consensus algorithms to dynamic data, or as the implementation of pseudo-Bayesian filtering algorithms in a sampling framework.