SCAFusion: A Multimodal 3D Detection Framework for Small Object Detection in Lunar Surface Exploration
This addresses the challenge of reliable small object detection for autonomous navigation in lunar robotic missions, representing an incremental improvement over existing methods.
The paper tackled the problem of detecting small and irregular objects like meteor fragments in lunar surface exploration by proposing SCAFusion, a multimodal 3D detection framework, which achieved 69.7% mAP on nuScenes and 90.93% mAP in simulated lunar environments, improving baselines by up to 11.5%.
Reliable and precise detection of small and irregular objects, such as meteor fragments and rocks, is critical for autonomous navigation and operation in lunar surface exploration. Existing multimodal 3D perception methods designed for terrestrial autonomous driving often underperform in off world environments due to poor feature alignment, limited multimodal synergy, and weak small object detection. This paper presents SCAFusion, a multimodal 3D object detection model tailored for lunar robotic missions. Built upon the BEVFusion framework, SCAFusion integrates a Cognitive Adapter for efficient camera backbone tuning, a Contrastive Alignment Module to enhance camera LiDAR feature consistency, a Camera Auxiliary Training Branch to strengthen visual representation, and most importantly, a Section aware Coordinate Attention mechanism explicitly designed to boost the detection performance of small, irregular targets. With negligible increase in parameters and computation, our model achieves 69.7% mAP and 72.1% NDS on the nuScenes validation set, improving the baseline by 5.0% and 2.7%, respectively. In simulated lunar environments built on Isaac Sim, SCAFusion achieves 90.93% mAP, outperforming the baseline by 11.5%, with notable gains in detecting small meteor like obstacles.