近似ベイズ推論による大域データ対応付けの分布推定
Distribution Estimation for Global Data Association via Approximate Bayesian Inference
曖昧なデータ対応付け問題に対し、近似ベイズ推論で複数の解モードを粒子として推定し、単一解への早期収束を避ける手法を提案。点群や物体マップの位置合わせで有効性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yixuan Jia, Mason B. Peterson, Qingyuan Li, Yulun Tian, Jonathan P. How
分類: cs.RO
原文アブストラクト
Global data association is an essential prerequisite for robot operation in environments seen at different times or by different robots. Repetitive or symmetric data creates significant challenges for existing methods, which typically rely on maximum likelihood estimation or maximum consensus to produce a single set of associations. However, in ambiguous scenarios, the distribution of solutions to global data association problems is often highly multimodal, and such single-solution approaches frequently fail. In this work, we introduce a data association framework that leverages approximate Bayesian inference to capture multiple solution modes to the data association problem, thereby avoiding premature commitment to a single solution under ambiguity. Our approach represents hypothetical solutions as particles that evolve according to a deterministic or randomized update rule to cover the modes of the underlying solution distribution. Furthermore, we show that our method can incorporate optimization constraints imposed by the data association formulation and directly benefit from GPU-parallelized optimization. Extensive simulated and real-world experiments with highly ambiguous data show that our method correctly estimates the distribution over transformations when registering point clouds or object maps.
