Real-time visual-inertial SLAM based on RGB-D image and point-line feature with loop closure constraint

Yanxi Chen; Qian Zong; Pengcheng Wei

doi:10.1117/12.3025973

1 April 2024 Real-time visual-inertial SLAM based on RGB-D image and point-line feature with loop closure constraint

Yanxi Chen, Qian Zong, Pengcheng Wei

Proceedings Volume 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023); 1308206 (2024) https://doi.org/10.1117/12.3025973
Event: 2023 4th International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology (MEMAT 2023), 2023, Guilin, China

Abstract

Conventional visual-inertial SLAM(VINS) primarily relying on point features often face challenges in indoor environments characterized by low textures, variable lighting, and fast motions. This is attributed to the difficulty in identifying a substantial number of dependable point features in such scenarios. Considering the abundance of structured features, such as line features in artificial environments, the RPL-VINS is introduced. This system not only harnesses line features to impose additional constraints but also seamlessly integrates them within the loop closure model. Using the Dempster-Shafer evidence theory, the system achieves a tight fusion of point-line loop closure outcomes, dramatically enhancing localization precision. Furthermore, the RGB-D camera's depth information facilitate direct recovery of feature-based 3D information, refining trajectory accuracy. By assimilating point-line features, the RPL-VINS's visual-inertial odometry (VIO) effectively trajectory and localizes the pose of each image frame. Experimental results on public OpenLORIS-Scene dataset reveal that RPL-VINS outperforms VINS-RGBD, PL-VIO, and PL-VINS in terms of performance. While ensuring real-time, it notably enhances trajectory precision and robustness, addressing the performance shortcomings of traditional algorithms in low-texture environments.

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

Citation Download Citation

Yanxi Chen, Qian Zong, and Pengcheng Wei "Real-time visual-inertial SLAM based on RGB-D image and point-line feature with loop closure constraint", Proc. SPIE 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023), 1308206 (1 April 2024); https://doi.org/10.1117/12.3025973

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Cameras

Visualization

Data fusion

Feature extraction

Mathematical optimization

Detection and tracking algorithms

Information fusion

Show All Keywords

Keywords/Phrases

Search In:

Publication Years