  Place recognition is an important capability for autonomously navigating
vehicles operating in complex environments and under changing conditions. It is
a key component for tasks such as loop closing in SLAM or global localization.
In this paper, we address the problem of place recognition based on 3D LiDAR
scans recorded by an autonomous vehicle. We propose a novel lightweight neural
network exploiting the range image representation of LiDAR sensors to achieve
fast execution with less than 2 ms per frame. We design a yaw-angle-invariant
architecture exploiting a transformer network, which boosts the place
recognition performance of our method. We evaluate our approach on the KITTI
and Ford Campus datasets. The experimental results show that our method can
effectively detect loop closures compared to the state-of-the-art methods and
generalizes well across different environments. To evaluate long-term place
recognition performance, we provide a novel dataset containing LiDAR sequences
recorded by a mobile robot in repetitive places at different times. The
implementation of our method and dataset are released here:
https://github.com/haomo-ai/OverlapTransformer
