智能科学与技术学报2025，Vol.7Issue(4)：433-443,11.DOI:10.11959/j.issn.2096-6652.202543
低纹理动态场景下基于特征增强的视觉惯导SLAM方法
Feature-enhanced visual-inertial SLAM method for low-texture dynamic environment
摘要
Abstract
To address the challenge of SLAM systems struggling to extract sufficient stable feature points and being prone to incorrect matching in low-texture dynamic environments,which leads to poor accuracy and robustness in pose estima-tion,this paper proposes a monocular visual-inertial SLAM system specifically designed for low-texture dynamic sce-narios.The proposed method achieves improved performance in feature extraction,matching,and dynamic feature point discrimination.First,a deep learning-based SuperPoint feature extraction and LightGlue feature matching modules are employed to replace the existing ORB-SLAM3 frontend,significantly enhancing the robustness of feature extraction and matching in weak texture areas.Second,by integrating YOLO-seg for dynamic region semantic segmentation and leveraging IMU pre-integration to estimate camera pose changes,a joint dynamic point removal mechanism is constructed to achieve finer-grained dynamic point filtering,thereby enhancing system accuracy and robustness in dynamic interference scenarios.Finally,the performance of the proposed method was validated through comparative experiments on public datasets and real-world scenarios,and the proposed system achieves a reduction of 88.4%or more in the root mean square error of absolute trajectory error,and 90%or more in the standard deviation in real-world low-texture dynamic scenarios,exhibiting supe-rior positioning accuracy and robustness when compared with existing visual and visual-inertial SLAM approaches.关键词
动态SLAM/低纹理/深度学习/特征提取/语义分割/IMU预积分Key words
dynamic SLAM/low-texture/deep learning/feature extraction/semantic segmentation/IMU pre-integration分类
信息技术与安全科学引用本文复制引用
解明扬,徐鑫,杨晨,余子锐,王垚..低纹理动态场景下基于特征增强的视觉惯导SLAM方法[J].智能科学与技术学报,2025,7(4):433-443,11.基金项目
国家重点研发计划项目(No.2023YFB4704400) （No.2023YFB4704400）
国家自然科学基金项目(No.62373186) （No.62373186）
江苏省自然科学基金项目(No.BK20231440) （No.BK20231440）
中央高校基本科研业务费(No.NZ2024-033) The National Key Research and Development Program of China(No.2023YFB4704400),The National Natural Science Foundation of China(No.62373186),The Natural Science Foundation of Jiangsu Province(No.BK20231440),The Funda-mental Research Funds for the Central Universities(No.NZ2024-033) （No.NZ2024-033）
