基于改进YOLO算法和多视点几何的VSLAM算法研究

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Tengwei Li, Linzheng Ye, Xijing Zhu, Shida Chuai, Jialong Wu, Wanqi Zhang, Wenlong Li
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引用次数: 0

摘要

视觉同步定位与绘图(VSLAM)是利用相机传感器进行环境感知和定位的技术,广泛应用于机器人、无人驾驶车辆等领域。传统的vslam通常假设静态环境,但是这种环境中的动态对象可能导致特征点不匹配,从而严重损害系统的准确性和鲁棒性。此外,现有的动态vslam还存在实时性不足等问题。为了应对动态环境的挑战,本文以ORB-SLAM2为框架,集成YOLOv5目标检测模块和动态特征抑制模块,引入了利用YOLO目标检测和运动几何深度融合的动态VSLAM系统,称为YOLO几何同步视觉定位与映射(YG-VSLAM)。本文的算法与其他动态算法有很大的不同,它以基本特征点为中心进行动态特征点的识别和消除。首先,算法的前端从输入图像中提取特征点。同时,目标检测模块识别动态类,圈定动态和静态区域。随后,采用六类区域分类策略将这些区域进一步划分为更详细的类别,如可疑动态类和静态类。最后,采用多视觉几何方法对每个区域内的特征点进行检测和剔除。本文使用TUM数据集进行了综合评估,评估了准确性和实时性。实验结果证明了该算法的有效性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Research on VSLAM Algorithm Based on Improved YOLO Algorithm and Multi-View Geometry

Research on VSLAM Algorithm Based on Improved YOLO Algorithm and Multi-View Geometry

Visual Simultaneous Localization and Mapping (VSLAM) uses camera sensors for environmental sensing and localization, widely applied in robotics, unmanned vehicles, and other sectors. Traditional VSLAMs typically assume static environments, but dynamic objects in such settings can cause feature point mismatches, significantly impairing system accuracy and robustness. Furthermore, existing dynamic VSLAMs suffer from issues like inadequate real-time performance. To tackle the challenges of dynamic environments, this paper adopts ORB-SLAM2 as the framework, integrates the YOLOv5 object detection module and a dynamic feature rejection module, and introduces a dynamic VSLAM system that leverages YOLO's object detection and motion geometry's depth fusion, termed YOLO Geometry Simultaneous Visual Localization and Mapping(YG-VSLAM). This paper's algorithm differs significantly from other dynamic algorithms, focusing on basic feature points for dynamic feature point identification and elimination. Initially, the algorithm's front-end extracts feature points from the input image. Concurrently, the target detection module identifies dynamic classes, delineating dynamic and static regions. Subsequently, a six-class region classification strategy is applied to further categorize these regions into more detailed categories, such as suspected dynamic and static classes. Finally, a multi-vision geometric method is employed to detect and eliminate feature points within each region. This paper conducts a comprehensive evaluation using the TUM data set, assessing both accuracy and real-time performance. The experimental outcomes demonstrate the algorithm's effectiveness and practicality.

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来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
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