Efficient Multimodal Belief Propagation for Robust SLAM Using Clustering Based Reparameterization

Seungwon Choi; Tae-Wan Kim

IROS 2021poster0 citations

Efficient Multimodal Belief Propagation for Robust SLAM Using Clustering Based Reparameterization

Abstract

Due to the presence of ambiguities caused by sensor noise and structural similarity, simultaneous localization and mapping (SLAM) observation models are typically multimodal. The multimodal inference process can be directly dealt with by belief propagation (BP) using weighted Gaussian mixture messages, but for efficiency, a combinatorial explosion of the complexity must be suitably relaxed. In this study, we present an effective multimodal BP SLAM for robust inference with ambiguities. Using Gaussian bandwidth mean shift and cluster-based reparameterization, we reduce the number of Gaussian components in each message due to the BP nature. The proposed algorithm reduces the number of components of the product by summarizing indistinguishable modes in weighted Gaussian mixtures and keeping only the significant modes, making BP computationally efficient.

BibTeX

@inproceedings{iros2021_efficientmultimo,
  title = {Efficient Multimodal Belief Propagation for Robust SLAM Using Clustering Based Reparameterization},
  author = {Seungwon Choi and Tae-Wan Kim},
  booktitle = {IROS 2021},
  year = {2021}
}