A review of multi-sensor fusion 3D object detection for autonomous driving

Junwei Zhao; Lixiang Li; Jun Dai

doi:10.1117/12.3032977

13 September 2024 A review of multi-sensor fusion 3D object detection for autonomous driving

Junwei Zhao, Lixiang Li, Jun Dai

Author Affiliations +

Proceedings Volume 13178, Eleventh International Symposium on Precision Mechanical Measurements; 1317826 (2024) https://doi.org/10.1117/12.3032977
Event: Eleventh International Symposium on Precision Mechanical Measurements, 2023, Guangzhou, China

Abstract

3D object detection is an important part of autonomous driving systems, which tasks are to accurately recognize 3D objects around the vehicle, such as cars, pedestrians, and bicycles. Current research is primarily focused on successfully integrating data from camera and LiDAR sensors to enhance detection accuracy and reliability, while overcoming the limits of using a single sensor. In this paper, we provide a review of 3D object detection methods for multi-sensor fusion. First, we introduce common camera and LiDAR sensors and their data processing methods. Subsequently, we classify the fusion algorithms into three categories: input fusion, feature fusion, and late fusion, based on different fusion strategies, and conduct an in-depth survey and discussion on them to analyze their respective advantages and disadvantages. In addition, we provide an overview of public datasets commonly used in 3D object detection. Finally, we provide an outlook on the future direction of multi-sensor fusion 3D object detection technology.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Junwei Zhao, Lixiang Li, and Jun Dai "A review of multi-sensor fusion 3D object detection for autonomous driving", Proc. SPIE 13178, Eleventh International Symposium on Precision Mechanical Measurements, 1317826 (13 September 2024); https://doi.org/10.1117/12.3032977

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KEYWORDS

Object detection

Point clouds

Cameras

LIDAR

Feature fusion

Sensors

Image fusion

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