TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
This work addresses computational efficiency in loop closure detection for autonomous driving systems, representing an incremental improvement over existing re-ranking methods.
The paper tackles the problem of high computational demand in 3D LiDAR place recognition for autonomous driving by proposing TReR, a lightweight transformer-based re-ranking approach that uses global descriptors. On the KITTI Odometry dataset, TReR showed robustness and efficiency compared to alphaQE and offered a good trade-off between robustness and efficiency compared to SGV.
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.