Vision-RADAR fusion for Robotics BEV Detections: A Survey

• Published in: 2023 IEEE Intelligent Vehicles Symposium (IV) pp. 1 - 7
• Main Author: Singh, Apoorv
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04.06.2023
• Subjects: autonomous driving; BEV Perception; camera radar fusion; computer vision; Costs; Object detection; Radar; Radar detection; review; Robot vision systems; robotics; sensor fusion; survey; Surveys; Three-dimensional displays
• ISSN: 2642-7214
• DOI: 10.1109/IV55152.2023.10186647

Due to the trending need of building autonomous robotic perception system, sensor fusion has attracted a lot of attention amongst researchers and engineers to make best use of cross-modality information. However, in order to build a robotic platform at scale we need to emphasize on autonomous robot platform bring-up cost as well. Cameras and radars, which inherently includes complementary perception information, has potential for developing autonomous robotic platform at scale. However, there is a limited work around radar fused with Vision, compared to LiDAR fused with vision work. In this paper, we tackle this gap with a survey on Vision-Radar fusion approaches for a BEV object detection system. First we go through the background information viz., object detection tasks, choice of sensors, sensor setup, benchmark datasets and evaluation metrics for a robotic perception system. Later, we cover per-modality (Camera and RADAR) data representation, then we go into detail about sensor fusion techniques based on sub-groups viz., early-fusion, deep-fusion, and late-fusion to easily understand the pros and cons of each method. Finally, we propose possible future trends for vision-radar fusion to enlighten future research. Regularly updated summary can be found at: https://github.com/ApoorvRoboticist/Vision-RADAR-Fusion-BEV-Survey