信息与控制2025，Vol.54Issue(6)：827-839,13.DOI:10.13976/j.cnki.xk.2024.3751
自适应关键帧选取的半直接动态RGB-D SLAM算法
Semi-direct Dynamic RGB-D SLAM Algorithm for Adaptive Keyframe Selection
摘要
Abstract
The traditional simultaneous localization and mapping(SLAM)algorithm suffers from exces-sive rigidity and redundancy in keyframe selection,and poor real-time performance and robustness in complex dynamic SLAM scenarios.We propose a lightweight,semi-direct,dynamic RGB-D SLAM algorithm to address these issues.First,we introduce the multi-level FAST(features from the accelerated segment test)feature removal algorithm,which combines a sparse alignment meth-od with the Shi-Tomasi scoring function.This approach eliminates dynamic object feature points,reduces computation time,and improves robustness in dynamic environments.Next,we propose an adaptive keyframe selection threshold strategy,which dynamically adjusts the threshold based on inter-frame information difference,enhancing positioning accuracy while reducing redundant keyframes.Finally,we incorporate a dense mapping module capable of removing dynamic objects while preserving the static environment in dynamic scenes.Experimental results on the TUM dynamic dataset and OpenLORIS-Scene dataset demonstrate that,in low-dynamic environments,the proposed algorithm im-proves accuracy by approximately 47％compared to ORB-SLAM3 and by approximately 90％in high-dynamic environments.Compared with four dynamic SLAM algorithms,our method reduces absolute trajectory error,mapping error,and average time per frame by at least 23％,42％and 26％,respectively.The dense mapping module also fully reconstructs the real scene with improved accuracy.关键词
自适应/动态SLAM/冗余关键帧/稠密建图Key words
adaptive/dynamic SLAM/redundant keyframe/dense mapping分类
信息技术与安全科学引用本文复制引用
廖中平,郝治国,谢烽,吕世宁..自适应关键帧选取的半直接动态RGB-D SLAM算法[J].信息与控制,2025,54(6):827-839,13.基金项目
国家自然科学基金项目(42301373) （42301373）
湖南省科技创新计划(重点研发计划)(2018SK2011) （重点研发计划）
长沙理工大学研究生科研创新项目(CSLGCX23137) （CSLGCX23137）
