A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques

被引：6

作者：

Marsh, Benedict ^{[1
]}

Sadka, Abdul Hamid ^{[1
]}

Bahai, Hamid ^{[2
]}

机构：

[1] Brunel Univ London, Inst Digital Futures, Kingston Ln, Uxbridge UB8 3PH, England

[2] Brunel Univ London, Inst Mat & Mfg, Kingston Ln, Uxbridge UB8 3PH, England

来源：

SENSORS | 2022年 / 22卷 / 23期

关键词：

sensor fusion; stereo; LiDAR; deep learning; NETWORK;

D O I：

10.3390/s22239364

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for.

引用

页数：19

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