AdaFusion: Visual-LiDAR Fusion With Adaptive Weights for Place Recognition

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 letter, an adaptive weighting visual-LiDAR fusion method, named AdaFusion, is proposed to learn the weights for both image and point cloud features. Features of these two modalities are thus contributed differently according to the current environmental situation. Weights are learned by the multi-scale 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 two-stage fusion approach to combine the 2D and 3D attention. Our work is tested on two public datasets. Experiments show that the adaptive weights help improve recognition accuracy and system robustness to varying environments while being efficient in runtime.