MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map Construction
Journal:
arXiv
Published Date:
Feb 5, 2025
Abstract
Map construction task plays a vital role in providing precise and
comprehensive static environmental information essential for autonomous driving
systems. Primary sensors include cameras and LiDAR, with configurations varying
between camera-only, LiDAR-only, or camera-LiDAR fusion, based on
cost-performance considerations. While fusion-based methods typically perform
best, existing approaches often neglect modality interaction and rely on simple
fusion strategies, which suffer from the problems of misalignment and
information loss. To address these issues, we propose MapFusion, a novel
multi-modal Bird's-Eye View (BEV) feature fusion method for map construction.
Specifically, to solve the semantic misalignment problem between camera and
LiDAR BEV features, we introduce the Cross-modal Interaction Transform (CIT)
module, enabling interaction between two BEV feature spaces and enhancing
feature representation through a self-attention mechanism. Additionally, we
propose an effective Dual Dynamic Fusion (DDF) module to adaptively select
valuable information from different modalities, which can take full advantage
of the inherent information between different modalities. Moreover, MapFusion
is designed to be simple and plug-and-play, easily integrated into existing
pipelines. We evaluate MapFusion on two map construction tasks, including
High-definition (HD) map and BEV map segmentation, to show its versatility and
effectiveness. Compared with the state-of-the-art methods, MapFusion achieves
3.6% and 6.2% absolute improvements on the HD map construction and BEV map
segmentation tasks on the nuScenes dataset, respectively, demonstrating the
superiority of our approach.
