Integrating point-line features and geomagnetic constraints in visual-inertial SLAM

doi:10.13474/j.cnki.11-2246.2026.0216

Abstract

Abstract: To address the problems of severe localization drift and high false positive/negative rates in loop closure detection under complex conditions in traditional visual-inertial simultaneous localization and mapping (VI-SLAM)systems,we propose a SLAM method that integrates point-line feature extraction and geomagnetic optimization.On one hand,the efficient line feature extraction algorithm Fast-EDLines is introduced into the existing visual-inertial odometry (VIO),accelerated using the AVX2 instruction set,along with a strategy of merging long line segments and eliminating short ones.On the other hand,in the loop closure detection process,magnetometer data from a 9-axis IMU is fused to apply geomagnetic constraints.Combined with a keyframe temporary buffer strategy,the visual matching threshold is dynamically adjusted to reduce false detections and missed detections.The proposed algorithm is tested on the public dataset VECtor Benchmark,and results show that the localization accuracy is improved by 7.0 times and 2.9 times compared to VINS-Mono and PL-VINS,respectively.Effectively improve the positioning accuracy and robustness of SLAM algorithm in complex environments.

Key words: visual-inertial SLAM, point-line feature detection, geomagnetic sequence matching, loop closure detection

CLC Number:

P228

WANG Yaohui, ZHANG Zuhao, CHEN Guoliang, WANG Teng. Integrating point-line features and geomagnetic constraints in visual-inertial SLAM[J]. Bulletin of Surveying and Mapping, 2026, 0(2): 97-103.

References

[1] 陈锐志,陈亮.基于智能手机的室内定位技术的发展现状和挑战[J].测绘学报,2017,46(10):1316-1326.
[2] 王怡欣,刘晖,钱闯,等.一种基于智能手机的实时高精度定位系统开发与车载应用测试[J].测绘通报,2022(10):56-61.
[3] 王腾,毕京学,陈国良,等.信道状态信息无线感知数据获取及应用综述[J].导航定位与授时,2024,11(1):10-29.
[4] 束明聪.基于地磁和惯性增强感知的室内视觉定位关键技术研究[D].徐州:中国矿业大学,2023.
[5] 李由.基于MEMS惯性传感器、WiFi、磁场特征的移动智能终端室内行人导航算法[D].武汉:武汉大学,2015.
[6] YOUSIF K,BAB-HADIASHAR A,HOSEINNEZHAD R.An overview to visual odometry and visual SLAM:applications to mobile robotics[J].Intelligent Industrial Systems,2015,1(4):289-311.
[7] 范启亮,丁度坤.基于多特征信息定位的机器人视觉SLAM算法[J].测绘通报,2025(4):14-19.
[8] LI Jinyu,YANG Bangbang,CHEN Danpeng,et al.Survey and evaluation of monocular visual-inertial SLAM algorithms for augmented reality[J].Virtual Reality & Intelligent Hardware,2019,1(4):386-410.
[9] 曾攀,潘树国,黄砺枭,等.基于加权预积分和快速初始化的惯性辅助单目前端模型[J].测绘通报,2019(8):8-13.
[10] MOURIKIS A I,ROUMELIOTIS S I.A multi-state constraint Kalman filter for vision-aided inertial navigation[J].Proceedings of 2007 IEEE International Conference on Robotics and Automation.[S.l.]:IEEE,2007:3565-3572.
[11] QIN Tong,LI Peiliang,SHEN Shaojie.VINS-mono:a robust and versatile monocular visual-inertial state estimator[J].IEEE Transactions on Robotics,2018,34(4):1004-1020.
[12] FU Qiang,WANG Jialong,YU Hongshan,et al.PL-VINS:real-time monocular visual-inertial SLAM with point and line features[EB/OL].[2025-06-10]https://arxiv.org/abs/2009.07462.
[13] WILLIAMS B,CUMMINS M,NEIRA J,et al.A comparison of loop closing techniques in monocular SLAM[J].Robotics and Autonomous Systems,2009,57(12):1188-1197.
[14] GALVEZ-LÓPEZ D,TARDOS J D.Bags of binary words for fast place recognition in image sequences[J].IEEE Transactions on Robotics,2012,28(5):1188-1197.
[15] MUR-ARTAL R,MONTIEL J M M,TARDÓS J D.ORB-SLAM:a versatile and accurate monocular SLAM system[J].IEEE Transactions on Robotics,2015,31(5):1147-1163.
[16] AKINLAR C,TOPAL C.EDLines:a real-time line segment detector with a false detection control[J].Pattern Recognition Letters,2011,32(13):1633-1642.
[17] DESOLNEUX A,MOISAN L,MOREL J M.from gestalt theory to image analysis:a probabilistic approach[M].New York:Springer New York,2008.
[18] GAO Ling,LIANG Yuxuan,YANG Jiaqi,et al.VECtor:a versatile event-centric benchmark for multi-sensor SLAM[J].IEEE Robotics and Automation Letters,2022,7(3):8217-8224.