同一地上車両の能動制御による空中車両のロバストな自己位置推定
Robust Localization of Aerial Vehicles via Active Control of Identical Ground Vehicles
UAVが複数の同一UGVを観測して自己位置を推定する際、UGVを最適配置することでラベルなし観測でも高精度に位置・姿勢を特定する手法を提案し、実機実験でcm級精度を達成した。
著者: Igor Spasojevic, Xu Liu, Ankit Prabhu, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
分類: cs.RO
原文アブストラクト
This paper addresses the problem of active collaborative localization in heterogeneous robot teams with unknown data association. It involves positioning a small number of identical unmanned ground vehicles (UGVs) at desired positions so that an unmanned aerial vehicle (UAV) can, through unlabelled measurements of UGVs, uniquely determine its global pose. We model the problem as a sequential two player game, in which the first player positions the UGVs and the second identifies the two distinct hypothetical poses of the UAV at which the sets of measurements to the UGVs differ by as little as possible. We solve the underlying problem from the vantage point of the first player for a subclass of measurement models using a mixture of local optimization and exhaustive search procedures. Real-world experiments with a team of UAV and UGVs show that our method can achieve centimeter-level global localization accuracy. We also show that our method consistently outperforms random positioning of UGVs by a large margin, with as much as a 90% reduction in position and angular estimation error. Our method can tolerate a significant amount of random as well as non-stochastic measurement noise. This indicates its potential for reliable state estimation on board size, weight, and power (SWaP) constrained UAVs. This work enables robust localization in perceptually-challenged GPS-denied environments, thus paving the road for large-scale multi-robot navigation and mapping.
