距離計測に基づく3次元相対姿勢推定における曖昧性を考慮したガウス和フィルタ
Gaussian-Sum Filter for Range-based 3D Relative Pose Estimation in the Presence of Ambiguities
距離計測による3次元相対姿勢推定の曖昧性をガウス混合モデルとして扱い、ガウス和フィルタで効率的に推定する手法を提案し、シミュレーションと実験で有効性を示した。
著者: Syed S. Ahmed, Mohammed A. Shalaby, Charles C. Cossette, Jerome Le Ny, James R. Forbes
分類: cs.RO
原文アブストラクト
Multi-robot systems must have the ability to accurately estimate relative states between robots in order to perform collaborative tasks, possibly with no external aiding. Three-dimensional relative pose estimation using range measurements oftentimes suffers from a finite number of non-unique solutions, or ambiguities. This paper: 1) identifies and accurately estimates all possible ambiguities in 2D; 2) treats them as components of a Gaussian mixture model; and 3) presents a computationally-efficient estimator, in the form of a Gaussian-sum filter (GSF), to realize range-based relative pose estimation in an infrastructure-free, 3D, setup. This estimator is evaluated in simulation and experiment and is shown to avoid divergence to local minima induced by the ambiguous poses. Furthermore, the proposed GSF outperforms an extended Kalman filter, demonstrates similar performance to the computationally-demanding particle filter, and is shown to be consistent.
