TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place
  Recognition
Tiago Barros Luís Garrote Martin Aleksandrov Cristiano Premebida Urbano J. Nunes
Autonomous driving systems often require reliable loop closure detection to
guarantee reduced localization drift. Recently, 3D LiDAR-based localization
methods have used retrieval-based place recognition to find revisited places
efficiently. However, when deployed in challenging real-world scenarios, the
place recognition models become more complex, which comes at the cost of high
computational demand. This work tackles this problem from an
information-retrieval perspective, adopting a first-retrieve-then-re-ranking
paradigm, where an initial loop candidate ranking, generated from a 3D place
recognition model, is re-ordered by a proposed lightweight transformer-based
re-ranking approach (TReR). The proposed approach relies on global descriptors
only, being agnostic to the place recognition model. The experimental
evaluation, conducted on the KITTI Odometry dataset, where we compared TReR
with s.o.t.a. re-ranking approaches such as alphaQE and SGV, indicate the
robustness and efficiency when compared to alphaQE while offering a good
trade-off between robustness and efficiency when compared to SGV.