Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception System
This addresses the perception problem for autonomous vehicles by improving sensor fusion, though it appears incremental as it builds on existing deep learning and fusion techniques.
The paper tackles 3D object detection for autonomous vehicles by fusing camera and radar data using a Cross-Domain Spatial Matching transformation, achieving superior performance over single-sensor solutions and competitive results with state-of-the-art fusion methods on the NuScenes dataset.
In this paper, we propose a novel approach to address the problem of camera and radar sensor fusion for 3D object detection in autonomous vehicle perception systems. Our approach builds on recent advances in deep learning and leverages the strengths of both sensors to improve object detection performance. Precisely, we extract 2D features from camera images using a state-of-the-art deep learning architecture and then apply a novel Cross-Domain Spatial Matching (CDSM) transformation method to convert these features into 3D space. We then fuse them with extracted radar data using a complementary fusion strategy to produce a final 3D object representation. To demonstrate the effectiveness of our approach, we evaluate it on the NuScenes dataset. We compare our approach to both single-sensor performance and current state-of-the-art fusion methods. Our results show that the proposed approach achieves superior performance over single-sensor solutions and could directly compete with other top-level fusion methods.