Vision-RADAR fusion for Robotics BEV Detections: A Survey

TL;DR

This survey reviews Vision-Radar fusion techniques for bird's-eye view object detection in robotics, emphasizing sensor choices, fusion methods, and future research directions, highlighting the potential for scalable autonomous robotic perception.

Contribution

It provides a comprehensive overview of Vision-Radar fusion approaches for BEV detection, addressing a gap in existing literature focused more on LiDAR-based methods.

Findings

Analyzes sensor fusion techniques: early, deep, late fusion.

Summarizes datasets and evaluation metrics for robotic perception.

Proposes future trends for Vision-Radar fusion research.

Abstract

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