不確かな入力に対応したワープドガウス過程による占有マップ構築
Warped Gaussian Processes Occupancy Mapping with Uncertain Inputs
姿勢推定の不確かさと知覚ノイズの非ガウス性を考慮し、ワープドガウス過程を用いてロボットの占有マップをより高精度に構築する手法を提案した。
著者: Maani Ghaffari Jadidi, Jaime Valls Miro, Gamini Dissanayake
分類: cs.RO
原文アブストラクト
In this paper, we study extensions to the Gaussian Processes (GPs) continuous occupancy mapping problem. There are two classes of occupancy mapping problems that we particularly investigate. The first problem is related to mapping under pose uncertainty and how to propagate pose estimation uncertainty into the map inference. We develop expected kernel and expected sub-map notions to deal with uncertain inputs. In the second problem, we account for the complication of the robot's perception noise using Warped Gaussian Processes (WGPs). This approach allows for non-Gaussian noise in the observation space and captures the possible nonlinearity in that space better than standard GPs. The developed techniques can be applied separately or concurrently to a standard GP occupancy mapping problem. According to our experimental results, although taking into account pose uncertainty leads, as expected, to more uncertain maps, by modeling the nonlinearities present in the observation space WGPs improve the map quality.
