SDG-SLAM: an improved visual semantic SLAM algorithm towards dynamic environments

Jun Dai; Minghao Yang; Junwei Zhao; Yundong Mei

doi:10.1117/12.3032516

13 September 2024 SDG-SLAM: an improved visual semantic SLAM algorithm towards dynamic environments

Jun Dai, Minghao Yang, Junwei Zhao, Yundong Mei

Author Affiliations +

Proceedings Volume 13178, Eleventh International Symposium on Precision Mechanical Measurements; 131780W (2024) https://doi.org/10.1117/12.3032516
Event: Eleventh International Symposium on Precision Mechanical Measurements, 2023, Guangzhou, China

Abstract

Visual simultaneous localization and mapping (SLAM) in dynamic scenarios is vulnerable to dynamic object factors, which can cause inaccurate pose estimation and limited robustness. To tackle this problem, we present a SLAM algorithm named SDG-SLAM, which combines improved visual semantic and geometric constraints to reject outliers and enhance performance. Firstly, the algorithm utilizes the neural network YOLOv5 (the fifth version of You Only Look Once) to obtain object’s prior semantics in the environment. In the meantime, the ORB feature points of keyframes are acquired, and the sparse optical flow tracking and matching are performed on non-key frames Secondly, adaptive initial dynamic point detection is implemented based on the epipolar constraints. Subsequently, the method of motion constraint posterior combined with semantic prior was used to screen and eliminate dynamic feature points. Finally, all static feature points from the environment are utilized, and these feature points are participated in the camera’s pose estimation. Substantial experiments performed on challenging public dynamic scenario datasets and outdoor dynamic scenarios demonstrate that SDG-SLAM significantly improves pose accuracy in scenes containing objects with different motion states. Specifically, compared to ORB-SLAM3, SDG-SLAM achieves more than 87% improvement in localization accuracy for high dynamic datasets and over 85% improvement for outdoor dynamic environments.

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

Citation Download Citation

Jun Dai, Minghao Yang, Junwei Zhao, and Yundong Mei "SDG-SLAM: an improved visual semantic SLAM algorithm towards dynamic environments", Proc. SPIE 13178, Eleventh International Symposium on Precision Mechanical Measurements, 131780W (13 September 2024); https://doi.org/10.1117/12.3032516

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
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Semantics

Cameras

Visualization

Detection and tracking algorithms

Optical flow

Environmental sensing

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years