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Open Access

ARTICLE

MG-SLAM: RGB-D SLAM Based on Semantic Segmentation for Dynamic Environment in the Internet of Vehicles

Fengju Zhang1, Kai Zhu2,*
1 School of Mechanical Engineering, Jiangsu University of Technology, Changzhou, 213001, China
2 School of Automobile and Traffic Engineering, Jiangsu University of Technology, Changzhou, 213001, China
* Corresponding Author: Kai Zhu. Email: email
(This article belongs to the Special Issue: Advanced Communication and Networking Technologies for Internet of Things and Internet of Vehicles)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.058944

Received 24 September 2024; Accepted 15 November 2024; Published online 09 December 2024

Abstract

The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.

Keywords

Visual SLAM; dynamic scene; semantic segmentation; GPU acceleration; key segmentation frame

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