マルチモーダル・マルチウェイ対応付けのための混合整数計画フレームワークMIXER
MIXER: A Principled Framework for Multimodal, Multiway Data Association
複数のモダリティと複数のデータ集合を同時に扱う対応付け問題を混合整数二次計画として定式化し、射影勾配降下による新たな連続緩和で効率的に解く手法を提案した。ロボティクスデータセットでF1スコアが最良手法比35%向上した。
著者: Parker C. Lusk, Ronak Roy, Kaveh Fathian, Jonathan P. How
分類: cs.RO
原文アブストラクト
A fundamental problem in robotic perception is matching identical objects or data, with applications such as loop closure detection, place recognition, object tracking, and map fusion. While the problem becomes considerably more challenging when matching should be done jointly across multiple, multimodal sets of data, the robustness and accuracy of matching in the presence of noise and outliers can be greatly improved in this setting. At present, multimodal techniques do not leverage multiway information, and multiway techniques do not incorporate different modalities, leading to inferior results. In contrast, we present a principled mixed-integer quadratic framework to address this issue. We use a novel continuous relaxation in a projected gradient descent algorithm that guarantees feasible solutions of the integer program are obtained efficiently. We demonstrate experimentally that correspondences obtained from our approach are more stable to noise and errors than state-of-the-art techniques. Tested on a robotics dataset, our algorithm resulted in a 35% increase in F1 score when compared to the best alternative.
