MDF-SLAM: Monocular Dense 3D Reconstruction Based on Depth Estimation

被引：0

作者：

Zhu, Zuojun ^{[1
]}

Xu, Xiangrong ^{[1
]}

Li, Yonggang ^{[1
]}

You, Tianya ^{[1
]}

Wang, Xiaoyi ^{[1
]}

Wang, Zhixiong ^{[2
]}

Wang, Haiyan ^{[3
]}

Xu, Shanshan ^{[3
]}

Rodic, Aleksandar ^{[4
]}

Petrovic, Petar B. ^{[4
]}

机构：

[1] Anhui Univ Technol, Sch Mech Engn, Maanshan 243002, Anhui, Peoples R China

[2] Osaka Univ, Sch Med, Osaka 5650871, Japan

[3] Maanshan Univ, Sch Osaka Med Engn, Maanshan 243032, Peoples R China

[4] Univ Belgrade, Kraljice Marije 16, Belgrade 11120, Serbia

来源：

2022 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2022) | 2022年

关键词：

D O I：

10.1109/ICARM54641.2022.9959213

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

SLAM is one of the most critical technologies for mobile robots. Vision-based SLAM techniques have recently become popular. In this study, a high-density reconstruction technique based on monocular camera, MDF-SLMA, is proposed to solve the constraints of stereo and RGB-D cameras. The DSO-based tracking thread is used for RGB input images. To estimate the depth of key frames, an updated deep learning technique is used. Attitude optimization and density mapping are based on the results of depth estimation. Experimental results show that the proposed method can achieve monocular density mapping and reduce SLAM trajectory error. MDF-SLAM lays the foundation for mobile robots to detect their surroundings and perform complex tasks.

引用

页码：787 / 792

页数：6

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