融合几何特征和语义信息的机器人动态场景视觉SLAM算法

doi:10.13474/j.cnki.11-2246.2025.0911

测绘通报 ›› 2025, Vol. 0 ›› Issue (9): 64-69.doi: 10.13474/j.cnki.11-2246.2025.0911

融合几何特征和语义信息的机器人动态场景视觉SLAM算法

何婷婷¹, 蒋祥龙¹, 何胜喜^2,3

1. 重庆科创职业学院智能制造与机器人学院, 重庆 402160;
2. 高端装备机械传动全国重点实验室, 重庆 400044;
3. 重庆长安汽车股份有限公司, 重庆 400023

收稿日期:2025-02-20 发布日期:2025-09-29
作者简介:何婷婷(1991—),女,讲师,研究方向为电气工程及自动化、智能控制技术等。E-mail:hett1991@163.com
基金资助:
2024年度重庆市教委科学技术研究青年项目(KJQN202405410)

Robot dynamic scene visual SLAM algorithm integrating geometric features and semantic information

HE Tingting¹, JIANG Xianglong¹, HE Shengxi^2,3

1. Intelligent Manufacturing and Robotics College, Chongqing College of Science and Creation, Chongqing 402160, China;
2. State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing 400044, China;
3. Chongqing Changan Automobile Co., Ltd., Chongqing 400023, China

Received:2025-02-20 Published:2025-09-29

摘要/Abstract

摘要： 针对机器人实现高精度视觉SLAM面临的动态环境下的稳健性差、定位误差大及特征丢失等问题,本文提出了一种融合几何特征和语义信息的视觉SLAM算法(Geo-Semantic SLAM)。该算法以ORB-SLAM2为基础,深度结合实例分割网络与几何特征提取技术,有效提升了系统在动态场景中的适应能力。具体而言,Geo-Semantic SLAM通过实例分割剔除动态目标的干扰,并设计了一种基于静态目标语义信息的相机姿态优化方法,显著弥补了特征点因动态剔除导致的损失。试验基于TUM数据集进行,结果表明,Geo-Semantic SLAM在动态环境下不仅具有更高的定位与建图精度,而且在多种场景中均优于传统算法及语义SLAM算法。

关键词: 几何特征, 语义信息, 动态场景, 机器人, SLAM

Abstract: To address the challenges of achieving high-accuracy visual SLAM in dynamic scenes,including low robustness,large localization errors,and feature loss,this paper proposes a visual SLAM algorithm that integrates geometric features and semantic information (Geo-Semantic SLAM).Built upon the ORB-SLAM2 framework,the proposed method deeply integrates semantic segmentation networks with geometric feature extraction techniques,significantly enhancing its adaptability to dynamic environments.Specifically,Geo-Semantic SLAM employs semantic segmentation to eliminate the influence of dynamic objects and introduces a camera pose optimization method based on the semantic information of static objects,effectively compensating for the feature loss caused by dynamic object removal.Experimental validation on the TUM dataset demonstrates that Geo-Semantic SLAM achieves superior localization and mapping accuracy in dynamic environments.Moreover,it consistently outperforms traditional algorithms and semantic SLAM methods across various scenarios.

Key words: geometric features, semantic information, dynamic scenes, robots, SLAM

中图分类号:

P237

何婷婷, 蒋祥龙, 何胜喜. 融合几何特征和语义信息的机器人动态场景视觉SLAM算法[J]. 测绘通报, 2025, 0(9): 64-69.

HE Tingting, JIANG Xianglong, HE Shengxi. Robot dynamic scene visual SLAM algorithm integrating geometric features and semantic information[J]. Bulletin of Surveying and Mapping, 2025, 0(9): 64-69.

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融合几何特征和语义信息的机器人动态场景视觉SLAM算法

Robot dynamic scene visual SLAM algorithm integrating geometric features and semantic information

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