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.

翻译：自动驾驶系统通常需要可靠的闭环检测来确保定位漂移的减小。近年来，基于3D LiDAR的定位方法采用检索式地点识别来高效地发现重访地点。然而，在具有挑战性的真实场景中部署时，地点识别模型变得更加复杂，这以高计算需求为代价。本研究从信息检索的角度解决这一问题，采用"先检索后重排序"的范式，通过提出的一种轻量级基于Transformer的重排序方法（TReR），对由3D地点识别模型生成的初始闭环候选排名进行重新排序。所提出的方法仅依赖全局描述符，与地点识别模型无关。在KITTI Odometry数据集上进行的实验评估表明，与alphaQE和SGV等最先进的重排序方法相比，TReR在鲁棒性上优于alphaQE，同时在与SGV的比较中提供了鲁棒性与效率之间的良好权衡。