光学精密工程2025，Vol.33Issue(8)：1259-1273,15.DOI:10.37188/OPE.20253308.1259
基于关键平面的异质特征融合视觉惯性SLAM系统
A visual inertial SLAM system based on key planes with heterogeneous feature fusion
摘要
Abstract
Planar feature is widely used in structured environments as a high-level geometric feature and is a good complement to most Simultaneous Localization and Mapping(SLAM)systems.In order to ad-dress the fact that new errors were introduced when fusing feature points with planar features and the possi-bility of planar degradation existed,we proposed a monocular visual inertial SLAM system that fuses het-erogeneous features in this paper.Firstly,feature points were extracted from grayscale images;secondly,the set of feature points was triangulated and the results of the triangulation were transformed to the world coordinate system.Next,the initialization process was modeled as a constrained optimization problem and solved with the alternating-direction multiplier method in a distributed fashion.Then,the similar planes were clustered and the planes were fitted with the proposed planar collision probability model to get the cor-responding bounded-plane parameters.Finally,geometric constraints on the plane features were intro-duced in the factor graph,and the camera motion as well as the plane parameters were simultaneously opti-mized by the error model.Compared with the typical visual inertial SLAM system VINS,the mean abso-lute trajectory error of the system proposed in this paper was reduced by 50%in the EuRoC dataset.The mean absolute trajectory error on the TUM-VI dataset was reduced by 40%.The method works stably and continuously in structured scenes and improves the localization accuracy and robustness in weakly tex-tured regions.关键词
SLAM/视觉惯性/分布式求解/有界平面提取/非线性优化Key words
Simultaneous Localization and Mapping(SLAM)/visual inertia/distributed solving/bound-ed plane extraction/nonlinear optimization分类
计算机与自动化引用本文复制引用
沈晔湖,何一凡,魏季坤,张大庆..基于关键平面的异质特征融合视觉惯性SLAM系统[J].光学精密工程,2025,33(8):1259-1273,15.基金项目
国家自然科学基金(No.51975394) （No.51975394）
江苏省研究生科研创新计划(No.SJCX24_1871) （No.SJCX24_1871）
江苏省自然科学基金(No.BK20211336) （No.BK20211336）
