CLIPPER: 初期推定不要のロバストデータ対応付け
CLIPPER: Robust Data Association without an Initial Guess
重み付きグラフの最も密なエッジ重み付きクリークを求める新たな最適化問題を定式化し、凸半正定値緩和と高速一次アルゴリズムCLIPPERを提案。点群位置合わせで95%の外れ値までロバスト。
著者: Parker C. Lusk, Jonathan P. How
分類: cs.RO, cs.LG
原文アブストラクト
Identifying correspondences in noisy data is a critically important step in estimation processes. When an informative initial estimation guess is available, the data association challenge is less acute; however, the existence of a high-quality initial guess is rare in most contexts. We explore graph-theoretic formulations for data association, which do not require an initial estimation guess. Existing graph-theoretic approaches optimize over unweighted graphs, discarding important consistency information encoded in weighted edges, and frequently attempt to solve NP-hard problems exactly. In contrast, we formulate a new optimization problem that fully leverages weighted graphs and seeks the densest edge-weighted clique. We introduce two relaxations to this problem: a convex semidefinite relaxation which we find to be empirically tight, and a fast first-order algorithm called CLIPPER which frequently arrives at nearly-optimal solutions in milliseconds. When evaluated on point cloud registration problems, our algorithms remain robust up to at least 95% outliers while existing algorithms begin breaking down at 80% outliers. Code is available at https://mit-acl.github.io/clipper.
