面向动态环境的紧耦合视觉惯性SLAM改进算法  

Improved Tightly-Coupled Visual-Inertial SLAMAlgorithm for Dynamic Environment

作　　者：郭瑞奇 修睿 孙勇 毛喆 GUO Ruiqi;XIU Rui;SUN Yong;MAO Zhe(Beijing Institute of Aerospace Control Devices,Beijing 100039,China)

机构地区：[1]北京航天控制仪器研究所,北京100039

出　　处：《计算机工程与应用》2025年第4期339-348,共10页Computer Engineering and Applications

基　　金：国家重点研发计划“智能传感器”重点专项(2022YFB3205005)。

摘　　要：SLAM(simultaneous localization and mapping)是无人载体实现自主导航定位的关键技术。针对传统视觉SLAM系统在动态场景下导航定位精度低的问题,在视觉SLAM系统的基础上引入惯性传感器(inertial measure-ment unit)。在ORB-SLAM3系统的基础上设计了一种面向动态环境的视觉惯性SLAM系统。提出一种基于向量场一致性(vector field consensus,VFC)的稀疏光流法来追踪图像的特征点并计算基础矩阵,分别利用光流对极几何约束和惯性传感器信息计算特征点的动态概率,提出一种联合的动态特征检测方法计算特征点的总动态概率,并将动态概率大于阈值的特征点进行剔除,在SLAM系统的前端实现了视觉信息与惯性运动信息的紧耦合。在数据集上的实验结果表明,该视觉惯性SLAM改进算法有良好的性能表现。SLAM(simultaneous localization and mapping)is a key technology for unmanned vehicles to achieve autono-mous navigation and positioning.In response to the problem of low accuracy of traditional visual SLAM system in dynamic environment,inertial measurement unit(IMU)is imported.A visual-inertial SLAM system for dynamic environment is designed based on the ORB-SLAM3 system.A sparse optical flow method based on vector field consensus(VFC)is pro-posed to track the feature points and calculate the fundamental matrix between images.The dynamic probability of feature points is calculated by epipolar geometry constraints of optical flow and IMU.A united dynamic feature detection algo-rithm is proposed to calculate the dynamic probability of feature points,and feature points that dynamic probability is greater than threshold are removed.This algorithm achieves tight coupling of visual information and inertial information in the front-end of the SLAM system.The experimental test results on the datasets indicate that the improved visual-inertial SLAM algorithm has good performance.

关 键 词：同时定位与地图创建(SLAM) 视觉惯性紧耦合 动态环境 向量场一致性 ORB-SLAM3 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]