  Place recognition is a fundamental task for robotic application, allowing
robots to perform loop closure detection within simultaneous localization and
mapping (SLAM), and achieve relocalization on prior maps. Current range
image-based networks use single-column convolution to maintain feature
invariance to shifts in image columns caused by LiDAR viewpoint change.However,
this raises the issues such as "restricted receptive fields" and "excessive
focus on local regions", degrading the performance of networks. To address the
aforementioned issues, we propose a lightweight circular convolutional
Transformer network denoted as CCTNet, which boosts performance by capturing
structural information in point clouds and facilitating crossdimensional
interaction of spatial and channel information. Initially, a Circular
Convolution Module (CCM) is introduced, expanding the network's perceptual
field while maintaining feature consistency across varying LiDAR perspectives.
Then, a Range Transformer Module (RTM) is proposed, which enhances place
recognition accuracy in scenarios with movable objects by employing a
combination of channel and spatial attention mechanisms. Furthermore, we
propose an Overlap-based loss function, transforming the place recognition task
from a binary loop closure classification into a regression problem linked to
the overlap between LiDAR frames. Through extensive experiments on the KITTI
and Ford Campus datasets, CCTNet surpasses comparable methods, achieving
Recall@1 of 0.924 and 0.965, and Recall@1% of 0.990 and 0.993 on the test set,
showcasing a superior performance. Results on the selfcollected dataset further
demonstrate the proposed method's potential for practical implementation in
complex scenarios to handle movable objects, showing improved generalization in
various datasets.
