CL-FusionBEV: A Cross-Attention Based Fusion Model for Camera and LiDAR in Bird’s Eye View Perception

In autonomous navigation, the ability to detect 3D objects from a Bird’s-Eye View (BEV) perspective is essential. Nevertheless, many obstacles remain before LiDAR and camera data can be effectively combined. We propose CL-FusionBEV, a novel framework for sensor fusion that enhances Three-dimensional object recognition in the BEV domain. This method structures LiDAR point clouds for improved spatial feature extraction while converting camera data into BEV format via an implicit learning technique. An implicit fusion network and a multi-modal cross-attention mechanism facilitate seamless sensor interaction, ensuring comprehensive feature integration. Additionally, a self-attention mechanism of BEV enhances broad-scale reasoning and data extraction, improving the detection of occluded and distant objects. By efficiently synchronising data from several sensors, the suggested method improves feature uniformity and resolves spatial inconsistencies. It further leverages adaptive feature selection to enhance robustness against sensor noise and varying conditions. We evaluate CL-FusionBEV on the nuScenes dataset, achieving achieved a 73.3% mAP and a 75.5% NDS on the nuScenes benchmark, with