非視覚情報を統合した確率的深度画像レジストレーション
Probabilistic Depth Image Registration incorporating Nonvisual Information
物体の動きに関する非視覚情報と、物体でないと観測された空間の情報も取り入れ、ベイズ枠組みで相対位置合わせの事後分布を推定する新しいレジストレーション手法を提案した論文。
著者: Manuel Wüthrich, Peter Pastor, Ludovic Righetti, Aude Billard, Stefan Schaal
分類: cs.RO, cs.CV
原文アブストラクト
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed to not be part of the object. Furthermore we are computing a posterior distribution over the relative alignment and not a point estimate as typically done in for example Iterative Closest Point (ICP). To our knowledge no existing algorithm meets these three conditions and we thus derive a novel registration algorithm in a Bayesian framework. Experimental results suggest that the proposed methods perform favorably in comparison to PCL implementations of feature mapping and ICP, especially if nonvisual information is available.
