Multi-Sensor Object Detection

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Multi-Sensor Object Detection

Zhang, Xinyu / Li, Jun / Li, Zhiwei / Liu, Huaping / Zhou, Mo / Wang, Li / Zou, Zhenhong

3D object detection is becoming an indispensable functional module for environmental perception in autonomous driving, and LiDAR-based detection methods have made remarkable progress in terms of accuracy. However, point clouds often fail to distinguish objects with similar structures, leading to false detection. Therefore, other sensors and LiDAR fusion are naturally considered a solution. Nevertheless, current fusion methods are either limited to poor precision or efficiency. To this end, this chapter proposes a plug-and-play module named RI-Fusion to achieve the effective fusion of LiDAR and camera, and the module can be easily accessed by existing LiDAR-based algorithms. Furthermore, a particular fusion method of RaDAR and 16-line LiDAR based on multimodal and multi-scale fusion is proposed, called $M^{2}$ \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M^{2}$$\end{document}-Fusion. The interaction is achieved by learning the features of each modality by exchanging the information of the intermediate feature layers with a self-attention mechanism. Experiments show that the method has better environmental adaptability and low cost.

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Dokumentinformationen

Titel :

Multi-Sensor Object Detection

Beteiligte:

Zhang, Xinyu (Autor:in) / Li, Jun (Autor:in) / Li, Zhiwei (Autor:in) / Liu, Huaping (Autor:in) / Zhou, Mo (Autor:in) / Wang, Li (Autor:in) / Zou, Zhenhong (Autor:in)

Erschienen in:

Multi-sensor Fusion for Autonomous Driving ; Kapitel : 4 ; 51-85

Erscheinungsdatum :

2023-05-11

Format / Umfang :

35 pages

ISBN :

978-981-99-3280-1 , 978-981-99-3279-5

DOI :

https://doi.org/10.1007/978-981-99-3280-1_4

Medientyp :

Aufsatz/Kapitel (Buch)