CLIPPER: A Graph-Theoretic Framework for Robust Data Association
This work provides a more robust and efficient data association method for applications in robotics and computer vision that deal with noisy and outlier-ridden data, such as point cloud registration.
This paper presents CLIPPER, a graph-theoretic framework for robust data association that tackles noise and outliers. It achieves 100% precision and 98% recall in 90% outlier regimes for point cloud registration, outperforming competitors that degrade at 70% outliers. CLIPPER maintains a runtime of 15 ms, while exact methods can take up to 24 s.
We present CLIPPER (Consistent LInking, Pruning, and Pairwise Error Rectification), a framework for robust data association in the presence of noise and outliers. We formulate the problem in a graph-theoretic framework using the notion of geometric consistency. State-of-the-art techniques that use this framework utilize either combinatorial optimization techniques that do not scale well to large-sized problems, or use heuristic approximations that yield low accuracy in high-noise, high-outlier regimes. In contrast, CLIPPER uses a relaxation of the combinatorial problem and returns solutions that are guaranteed to correspond to the optima of the original problem. Low time complexity is achieved with an efficient projected gradient ascent approach. Experiments indicate that CLIPPER maintains a consistently low runtime of 15 ms where exact methods can require up to 24 s at their peak, even on small-sized problems with 200 associations. When evaluated on noisy point cloud registration problems, CLIPPER achieves 100% precision and 98% recall in 90% outlier regimes while competing algorithms begin degrading by 70% outliers. In an instance of associating noisy points of the Stanford Bunny with 990 outlier associations and only 10 inlier associations, CLIPPER successfully returns 8 inlier associations with 100% precision in 138 ms. Code is available at https://mit-acl.github.io/clipper.