  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
