Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments

Alberto García-Hernández, Riccardo Giubilato, Klaus H. Strobl, Javier Civera, Rudolph Triebel
3/20/2024
cs.CVcs.RO

Official code: DLR-RM/UMF (23 stars, MIT, last commit 2025-04-29).

Abstract

Perceptual aliasing and weak textures pose significant challenges to the task of place recognition, hindering the performance of Simultaneous Localization and Mapping (SLAM) systems. This paper presents a novel model, called UMF (standing for Unifying Local and Global Multimodal Features) that 1) leverages multi-modality by cross-attention blocks between vision and LiDAR features, and 2) includes a re-ranking stage that re-orders based on local feature matching the top-k candidates retrieved using a global representation. Our experiments, particularly on sequences captured on a planetary-analogous environment, show that UMF outperforms significantly previous baselines in those challenging aliased environments. Since our work aims to enhance the reliability of SLAM in all situations, we also explore its performance on the widely used RobotCar dataset, for broader applicability. Code and models are available at https://github.com/DLR-RM/UMF

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Code Implementations(1)

DLR-RM/UMFOfficial100%
MIT
231PythonSep 14, 20231 years agoMIT

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Cite this paper

@article{garcahernndez2024unifying,
  title  = {Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments},
  author = {Alberto García-Hernández and Riccardo Giubilato and Klaus H. Strobl and Javier Civera and Rudolph Triebel},
  year   = {2024},
  eprint = {2403.13395},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.13395v1}
}

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