Outlier-Robust Geometric Perception: A Novel Thresholding-Based Estimator with Intra-Class Variance Maximization

• URL: http://arxiv.org/abs/2204.01324v2
• Date: Sun, 30 Jun 2024 16:25:48 GMT
• Title: Outlier-Robust Geometric Perception: A Novel Thresholding-Based Estimator with Intra-Class Variance Maximization
• Authors: Lei Sun,
• Abstract summary: We present a novel general-purpose robust estimator TIVM (Thresholding with Intra-class Variance Maximization) It can collaborate with standard non-minimal solvers to efficiently reject outliers for geometric perception problems. Our estimator can retain approximately the same level of robustness even when the inlier-noise statistics of the problem are fully unknown.
• Score: 4.3487328134753795
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Geometric perception problems are fundamental tasks in robotics and computer vision. In real-world applications, they often encounter the inevitable issue of outliers, preventing traditional algorithms from making correct estimates. In this paper, we present a novel general-purpose robust estimator TIVM (Thresholding with Intra-class Variance Maximization) that can collaborate with standard non-minimal solvers to efficiently reject outliers for geometric perception problems. First, we introduce the technique of intra-class variance maximization to design a dynamic 2-group thresholding method on the measurement residuals, aiming to distinctively separate inliers from outliers. Then, we develop an iterative framework that robustly optimizes the model by approaching the pure-inlier group using a multi-layered dynamic thresholding strategy as subroutine, in which a self-adaptive mechanism for layer-number tuning is further employed to minimize the user-defined parameters. We validate the proposed estimator on 3 classic geometric perception problems: rotation averaging, point cloud registration and category-level perception, and experiments show that it is robust against 70--90\% of outliers and can converge typically in only 3--15 iterations, much faster than state-of-the-art robust solvers such as RANSAC, GNC and ADAPT. Furthermore, another highlight is that: our estimator can retain approximately the same level of robustness even when the inlier-noise statistics of the problem are fully unknown.

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