论文标题
快船：强大数据关联的图理论框架
CLIPPER: A Graph-Theoretic Framework for Robust Data Association
论文作者
论文摘要
我们提出快船（一致的链接，修剪和成对误差纠正），这是在存在噪声和异常值的情况下稳健数据关联的框架。我们使用几何一致性概念在图理论框架中提出问题。使用此框架的最先进的技术利用了不能很好地扩展到大型问题的组合优化技术，或者使用启发式近似值，在高噪声，高淘汰较高的机制中产生较低准确性。相比之下，快船使用了组合问题的放松，并返回的解决方案可以保证与原始问题的最佳相对应。通过有效的投影梯度上升方法实现低时间复杂性。实验表明，快船的运行时间始终保持15毫秒，即使在200个关联的小型问题上，精确的方法也需要高达24 s。当对嘈杂的点云注册问题进行评估时，Clipper可以在90％的离群方案中达到100％的精度，而竞争算法的90％召回算法开始降低70％的离群值。在将斯坦福兔子的嘈杂点与990个离群协会相关联，只有10个Inlier关联，Clipper在138毫秒内成功地返回了8个Inlier关联和100％的精度。代码可在https://mit-acl.github.io/clipper上找到。
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.
