Bayesian Nonparametric Object Association for Semantic SLAM

Abstract

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.