A Hyperdimensional One Place Signature to Represent Them All: Stackable Descriptors For Visual Place Recognition

TL;DR

This paper introduces Hyperdimensional One Place Signatures (HOPS), a scalable, compute-efficient method that fuses multiple environmental descriptors to enhance visual place recognition performance across diverse conditions.

Contribution

HOPS provides a novel hyperdimensional computing framework for fusing multiple descriptors, improving robustness, scalability, and enabling new applications without additional compute costs.

Findings

Significantly improves recall across datasets and methods

Enables descriptor dimensionality reduction without performance loss

Supports stacking synthetic images and coarse localization

Abstract

Visual Place Recognition (VPR) enables coarse localization by comparing query images to a reference database of geo-tagged images. Recent breakthroughs in deep learning architectures and training regimes have led to methods with improved robustness to factors like environment appearance change, but with the downside that the required training and/or matching compute scales with the number of distinct environmental conditions encountered. Here, we propose Hyperdimensional One Place Signatures (HOPS) to simultaneously improve the performance, compute and scalability of these state-of-the-art approaches by fusing the descriptors from multiple reference sets captured under different conditions. HOPS scales to any number of environmental conditions by leveraging the Hyperdimensional Computing framework. Extensive evaluations demonstrate that our approach is highly generalizable and consistently improves recall performance across all evaluated VPR methods and datasets by large margins. Arbitrarily fusing reference images without compute penalty enables numerous other useful possibilities, three of which we demonstrate here: descriptor dimensionality reduction with no performance penalty, stacking synthetic images, and coarse localization to an entire traverse or environmental section.