  Recent years have witnessed the increasing application of place recognition
in various environments, such as city roads, large buildings, and a mix of
indoor and outdoor places. This task, however, still remains challenging due to
the limitations of different sensors and the changing appearance of
environments. Current works only consider the use of individual sensors, or
simply combine different sensors, ignoring the fact that the importance of
different sensors varies as the environment changes. In this paper, an adaptive
weighting visual-LiDAR fusion method, named AdaFusion, is proposed to learn the
weights for both images and point cloud features. Features of these two
modalities are thus contributed differently according to the current
environmental situation. The learning of weights is achieved by the attention
branch of the network, which is then fused with the multi-modality feature
extraction branch. Furthermore, to better utilize the potential relationship
between images and point clouds, we design a twostage fusion approach to
combine the 2D and 3D attention. Our work is tested on two public datasets, and
experiments show that the adaptive weights help improve recognition accuracy
and system robustness to varying environments.
