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Not Only Look But Infer: Multiple Hypothesis Clustering of Data Association Inference for Semantic SLAM | IEEE Journals & Magazine | IEEE Xplore

Not Only Look But Infer: Multiple Hypothesis Clustering of Data Association Inference for Semantic SLAM


Abstract:

In semantic visual simultaneous localization and mapping (VSLAM), accurate data association of semantic measurement from visual sensor is very crucial for robot state est...Show More

Abstract:

In semantic visual simultaneous localization and mapping (VSLAM), accurate data association of semantic measurement from visual sensor is very crucial for robot state estimation and scene reconstruction. However, most of the related works assume a simple world for semantic association. It is still a challenge to deal with the ambiguity of data association in a cluttered environment. In this article, we propose a novel approach to reduce the uncertainty of data association via multiple hypothesis Dirichlet process (MHDP). The posterior distribution of data association is inferred by Dirichlet process (DP) first. Ambiguous associations from the distribution are tackled by a hypothesis tree, and hypothesis testing-based ambiguity judgment is then proposed for each object measurement to provide a strategy for branch growing of the hypothesis tree. Moreover, the proposed data association approach is integrated with a geometric featured-based simultaneous localization and mapping (SLAM) system in a tightly coupled way. The qualitative and quantitative evaluation on simulated and real-world public data sets demonstrates the robustness and effectiveness of our approach compared to other data association methods and the state-of-the-art SLAM system.
Article Sequence Number: 3515409
Date of Publication: 22 April 2021

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