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Bayesian Nonparametric Object Association for Semantic SLAM by Zhang Jianbo; Liang Yuan; Teng Ran; Qing Tao; Li He is a Engineering article available to read on EtoBox.

What is Bayesian Nonparametric Object Association for Semantic SLAM about?

Semantic simultaneous localization and mapping (SLAM) can provide the foundation for robots to perform more advanced tasks than traditional geometric SLAM. Object association is a crucial factor in semantic SLAM. Previous work utilizes observations at the current moment and a single geometric feature to associate objects. The number and measurement of objects change over time. The ambiguity of object association reduces the performance of semantic SLAM. In this letter, we present a Bayesian Nonparametric model approach for object association. The temporal Dirichlet process mixture (TDPM) model is introduced to provide prior information. We couple the evolution of time with the object association, which considers the influence of historical association information on the current association results. Besides, we propose a Multi-Rules Likelihood (MRL) model to eliminate the ambiguity of object association by considering multiple object properties. We validate our approach both in simulation and real data from the KITTI dataset. The experimental results show that our approach improves the performance of object association and semantic SLAM.

Who reads Bayesian Nonparametric Object Association for Semantic SLAM?

Author

Zhang Jianbo; Liang Yuan; Teng Ran; Qing Tao; Li He

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)