Flow separation for fast and robust stereo odometry by Ni, Kai, Dellaert, Frank, Kaess, Michael

“…The overall algorithm is faster than estimating the motion in one step by a standard RANSAC-based three-point algorithm. And in contrast to other visual odometry work, we avoid the problem of nearly degenerate data, under which RANSAC is known to return inconsistent results. …”
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Outlier-Robust Multi-View Triangulation Using Graduated Non-Convexity for Space Vehicle Navigation by Mitchell, Adriana Macieira

“…We apply Yang et al.’s (2020) Graduated Non-Convexity (GNC) algorithm to three chosen multi-view triangulation solvers and improve the best performing solver’s robustness to 50% outliers which outperforms the current state-of-the-art outlier removal method RANSAC. We apply the robust multi-view triangulation solver to a simulated lunar landing trajectory and reported the variance of the returned 3D error to verify the accuracy of the estimate. …”
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Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection by Yang, Heng, Antonante, Pasquale, Tzoumas, Vasileios, Carlone, Luca

“…Our solvers are robust to 70-80% of outliers, outperform RANSAC, are more accurate than specialized local solvers, and faster than specialized global solvers. …”
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A Quaternion-Based Certifiably Optimal Solution to the Wahba Problem With Outliers by Yang, Heng, Carlone, Luca

“…We validate the proposed algorithm, named QUASAR (QUAternion-based Semidefinite relAxation for Robust alignment), in both synthetic and real datasets showing that the algorithm outperforms RANSAC, robust local optimization techniques, global outlier-removal procedures, and Branch-and-Bound methods. …”
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A Polynomial-time Solution for Robust Registration with Extreme Outlier Rates by Yang, Heng, Carlone, Luca

“…We validate the proposed algorithm, named TEASER (Truncated least squares Estimation And SEmidefinite Relaxation), in standard registration benchmarks showing that the algorithm outperforms RANSAC and robust local optimization techniques, and favorably compares with Branch-and-Bound methods, while being a polynomial-time algorithm. …”
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Outlier-Robust Spatial Perception: Hardness, General-Purpose Algorithms, and Guarantees by Tzoumas, Vasileios, Antonante, Pasquale, Carlone, Luca

“…Although techniques to handle outliers do exist, they can fail in unpredictable manners (e.g., RANSAC, robust estimators), or can have exponential runtime (e.g., branch-and-bound). …”
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Wide-area egomotion from omnidirectional video and coarse 3D structure by Koch, Olivier (Olivier A.)

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TEASER: Fast and Certifiable Point Cloud Registration by Yang, Heng, Shi, Jingnan, Carlone, Luca

“…Moreover, we test their performance on standard benchmarks, object detection datasets, and the 3DMatch scan matching dataset, and show that 1) both algorithms dominate the state-of-the-art (e.g., RANSAC, branch-&amp;-bound, heuristics) and are robust to more than <formula><tex>$\text{99\%}$</tex></formula> outliers when the scale is known, 2) TEASER++ can run in milliseconds and it is currently the fastest robust registration algorithm, and 3) TEASER++ is so robust it can also solve problems without correspondences (e.g., hypothesizing all-to-all correspondences), where it largely outperforms ICP and it is more accurate than Go-ICP while being orders of magnitude faster. …”
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