Parameterization of Vector Symbolic Approach for Sequence Encoding Based Visual Place Recognition

Abstract

Sequence-based methods for visual place recognition (VPR) have great importance due to their ability of additional information capture through the sequences compared to single image comparison. Vector symbolic architecture (VSA) started to gain attention within these methods due to the unique capabilities for representing variable-length sequences using single high-dimensional vectors. But the effect of different sequence parameters for the visual place recognition task is yet to be explored. In this work, we explore the parametrization of sequence encoding with VSA in the SeqNet variant of sequence-based visual place recognition and introduce a new hierarchical VPR method, which utilizes the proposed parametrization. We show that with our parametrization the VSA realization of sequence-based visual place recognition achieves on par results to conventional algorithms, while featuring the capability of being implemented on novel neuromorphic hardware for efficient execution.