ガウス過程潜在場整形による効率的な占有マッピング
Towards Efficient Occupancy Mapping via Gaussian Process Latent Field Shaping
ガウス過程の潜在場を直接操作し、センサ視野形状に基づく自由空間の事前情報を効率的に統合する占有マッピング手法を提案した。
詳しい要約
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著者: Cedric Le Gentil, Cedric Pradalier, Timothy D. Barfoot
分類: cs.RO
原文アブストラクト
Occupancy mapping has been a key enabler of mobile robotics. Originally based on a discrete grid representation, occupancy mapping has evolved towards continuous representations that can predict the occupancy status at any location and account for occupancy correlations between neighbouring areas. Gaussian Process (GP) approaches treat this task as a binary classification problem using both observations of occupied and free space. Conceptually, a GP latent field is passed through a logistic function to obtain the output class without actually manipulating the GP latent field. In this work, we propose to act directly on the latent function to efficiently integrate free space information as a prior based on the shape of the sensor's field-of-view. A major difference with existing methods is the change in the classification problem, as we distinguish between free and unknown space. The `occupied' area is the infinitesimally thin location where the class transitions from free to unknown. We demonstrate in simulated environments that our approach is sound and leads to competitive reconstruction accuracy.
