火力与指挥控制2026，Vol.51Issue(3)：59-65,7.DOI:10.3969/j.issn.1002-0640.2026.03.008
基于改进词袋模型的自驾车辆视觉SLAM闭环检测
Closed-loop Detection of Visual SLAM in Autonomous Vehicles Based on an Improved Bag-of-words Model
摘要
Abstract
A closed-loop detection method for simultaneous localization and mapping(SLAM)of autonomous vehicles based on an improved bag-of-words(BoW)model is proposed to address the problem of increasing cumulative positioning errors during SLAM,which leads to the inability to construct globally consistent maps.The traditional BoW model is optimized by generating a vocabulary tree through the Canopy K-means clustering algorithm.A match is considered successful when the similarity between the current image and the candidate image is greater than the threshold.Double validation is performed on successfully matched images using the temporal validation method and key region covariance matrix method.The effectiveness of the proposed method is evaluated on both the public KITTI dataset and a self-collected dataset.The precision-recall curves show that compared to the original algorithm,the improved algorithm improves the recall rate by 12%while maintaining an precision of 80%.关键词
自动驾驶/同时定位与建图/词袋模型/词汇树/时序法/双重验证/闭环检测Key words
autonomous vehicles/SLAM/BoW model/vocabulary tree/temporal validation/double validation/closed-loop detection分类
信息技术与安全科学引用本文复制引用
温国强,王泽名,关志伟,臧鹏涛,窦汝振..基于改进词袋模型的自驾车辆视觉SLAM闭环检测[J].火力与指挥控制,2026,51(3):59-65,7.基金项目
天津市教委科研基金资助项目(2020KJ086) （2020KJ086）
