Map Building By Multi-dimensional Scaling of Co-visibility Data  Extentions by SMACOF Method and Distance Function Estimation

Yairi Takehisa, Maeno Toshiaki

doi:10.1527/tjsai.22.342

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同時可視データへの多次元尺度構成法の適用による地図作成　　ＳＭＡＣＯＦ法と距離関数推定による拡張
ドウジカシデータエノタジゲンシャクドコウセイホウノテキヨウニヨルチズサクセイ SMACOFホウトキョリカンスウスイテイニヨルカクチョウ
Extentions by SMACOF Method and Distance Function Estimation
SMACOF法と距離関数推定による拡張

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Abstract

Covisibility-based mapping is a paradigm for robotic map building research in which a mobile robot estimates multiple object positions only from ``covisibility'' information, i.e., ``which objects were recognized at a time''. In previous studies on this problem, a solution based on a combination of heuristics - ``closely located objects are likely to be seen simultaneously more often than distant objects'' and Multi-Dimensional Scaling (MDS) was proposed, and it was shown that qualitative spatial relationships among objects are learned with high accuracy by this method. However, theoretical validity of the heuristics has not been sufficiently discussed in these studies. Besides, the existing method has a defect that the quantitative accuracy of built maps is very low. In this paper, we first prove that the heuristics is generally valid in a certain condition, and then present several enhancements to the original method in order to improve the quantitative accuracy of the maps. In the experiments, it was found these enhacements are quite effective.

Journal

Transactions of the Japanese Society for Artificial Intelligence

Transactions of the Japanese Society for Artificial Intelligence 22 (3), 342-352, 2007

The Japanese Society for Artificial Intelligence

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Map Building By Multi-dimensional Scaling of Co-visibility Data Extentions by SMACOF Method and Distance Function Estimation