基于鸟瞰图的空间-通道注意力多传感器融合

Spatial-Channel Attention Multi-sensor Fusion Based on Bird′s-Eye View

基于鸟瞰图(Bird′s-Eye View,BEV)的目标感知算法现正成为研究热点,但少有针对BEV的多传感器融合.因此,文中提出基于空间-通道注意力的多传感器融合模块,对不同模态的特征数据增加局部注意力机制,能有效修正多传感器之间的空间误差.利用转置注意力操作,充分融合图像和点云数据,消解不同模态语义信息之间的异质性,使融合的BEV特征在不引入空间偏差的同时,有效结合多传感器各自的独特信息,实现更全面准确的感知.在nuScenes数据集上的实验以及大量的消融实验表明,文中融合模块能有效提升目标检测算法的精度,可视化结果展现融合后的特征具有更完整、准确的特征信息,可明显提升对远处物体的检测.

Object perception based on bird′s-eye view(BEV)is one of hot issues,but studies on multi-sensor fusion for BEV are still insufficient.Therefore,a multi-sensor fusion module based on spatial-channel attention is proposed.Spatial errors between multiple sensors can be effectively corrected by adding local attention mechanisms to features of different modalities.By using transpose attention operations,the image and point cloud data are fully integrated to resolve the heterogeneity between different modal semantics.Consequently,the fused BEV features achieves more comprehensive and accurate perception by effectively combining the unique information of each sensor without introducing spatial misalignment.Experiment on nuScenes dataset and extensive ablation experiments show that the proposed fusion module effectively improves the accuracy of object detection.Visualization results demonstrate that the fused features can capture more complete and accurate information,especially in distant objects detection.