智能手机多源数据融合增强的地磁SLAM方法

doi:10.11947/j.AGCS.2025.20250266

测绘学报 ›› 2025, Vol. 54 ›› Issue (10): 1812-1825.doi: 10.11947/j.AGCS.2025.20250266

智能手机多源数据融合增强的地磁SLAM方法

邵克凡¹(), 李增科¹(), 孙猛¹, 刘振彬², 吴祺¹

^1.中国矿业大学环境与测绘学院，江苏　徐州　221116
^2.河南工程学院土木工程学院，河南　郑州　451191

收稿日期:2025-07-14 修回日期:2025-09-15 出版日期:2025-11-14 发布日期:2025-11-14
通讯作者: 李增科 E-mail:kefanshao@cumt.edu.cn;zengkeli@yeah.net
作者简介:邵克凡（1998—），男，博士生，主要研究方向为多源传感器融合与室内应急定位。E-mail：kefanshao@cumt.edu.cn
基金资助:
国家自然科学基金(42274020);江苏省科技计划(BE2023692);国家自然科学基金青年项目(42304047)

A geomagnetic SLAM method enhanced by multi-source data fusion based on smartphones

Kefan SHAO¹(), Zengke LI¹(), Meng SUN¹, Zhenbin LIU², Qi WU¹

^1.School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
^2.College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, China

Received:2025-07-14 Revised:2025-09-15 Online:2025-11-14 Published:2025-11-14
Contact: Zengke LI E-mail:kefanshao@cumt.edu.cn;zengkeli@yeah.net
About author:SHAO Kefan (1998—), male, PhD candidate, majors in multi-sensor fusion and indoor emergency positioning. E-mail: kefanshao@cumt.edu.cn
Supported by:
The National Natural Science Foundation of China(42274020);The Science and Technology Planning Project of Jiangsu Province(BE2023692);The National Natural Science Foundation for Young Scientists of China(42304047)

摘要/Abstract

摘要：

地磁同步定位与构图（simultaneously localization and mapping，SLAM）无须先验地磁指纹库，即可实现基于智能手机的未知室内环境定位。然而，智能手机地磁SLAM仍受限于惯性定位精度差、因子图优化算法动态适应能力不足及大型场景SLAM应用系统性能恶化等技术瓶颈。为解决此问题，本文通过设计方差时序递增机制和多源关键数据帧，提出一种面向大型室内场景的地磁SLAM增强优化算法。首先，为了提高惯性定位精度，本文挖掘行人运动过程中呈现出的特征规律构建观测方程，并融合地磁环境信息实现手机端地磁SLAM。然后，针对因子图优化算法动态适应能力不足，采用前端卡尔曼滤波与后端因子图优化相结合的定位框架提升时效性，同时设计方差时序递增机制，动态融合不同定位方法。最后，为了缓解大型场景地磁SLAM性能恶化，在时序维度上扩展关键帧概念和特征表达能力，有效缓解大型场景地磁误匹配问题；结合多源数据设计稳健回环探测与匹配算法，构建关键帧评分机制降低空间密度，从而提高算法效率。试验结果表明，本文实现了大型室内场景闭环情形下的地磁SLAM，相比惯性定位和经典地磁SLAM，本文提出的地磁SLAM增强优化方法的位置均方根误差降低了18%~67%；并且在仅利用标准方法22.6%的关键帧数量的前提下，本文方法仍能保持更高精度、更平滑的定位结果；通过试验探究了参数设置对定位精度和运行时间的影响，明确了地磁图构建首要因素基函数体素网格边长。

关键词: 地磁SLAM, 多源融合, 惯性定位, 因子图优化, 扩展卡尔曼滤波

Abstract:

Geomagnetic simultaneous localization and mapping (SLAM) enables smartphone-based positioning in unknown indoor environments without requiring a pre-established geomagnetic fingerprint database. However, geomagnetic SLAM on smartphones still faces technical bottlenecks, including low inertial positioning accuracy, insufficient adaptability of factor graph optimization (FGO) under dynamic conditions, and performance deterioration in large-space SLAM applications. To address these challenges, this paper proposes an enhanced optimization algorithm for geomagnetic SLAM in large-space indoor environments by designing a variance-based temporal increment mechanism and presenting multi-source data key frames. First, to enhance inertial positioning accuracy, this paper explores the characteristic patterns exhibited during the movement of pedestrians to construct observation equations, and integrate them with geomagnetic environmental information to achieve smartphone-based geomagnetic SLAM. Second, to overcome the insufficient dynamic adaptability of the FGO, a hybrid positioning framework is adopted that combines front-end Kalman filter and back-end FGO, improving timeliness. Meanwhile, a variance-based time series increasing mechanism is designed to dynamically integrate different positioning methods. Third, to alleviate the performance degradation of geomagnetic SLAM in large-space indoor environments, the concept of keyframes and their feature representation is extended along the temporal dimension, effectively alleviating the problem of large-space geomagnetic mismatching. A robust loop detection and matching algorithm is developed using multi-source data, and also a keyframe scoring mechanism is constructed to reduce spatial density and improve computational efficiency. Experimental results demonstrate that the proposed method achieves reliable geomagnetic SLAM in a large-space indoor loop-closing scene. Compared with standalone inertial positioning and classical geomagnetic SLAM, the proposed enhanced approach reduces the root mean square positioning error by 18%~67%. Moreover, it requires only 22.6% of the keyframes used by the standard approach, while still achieving higher accuracy and smoother localization results. Furthermore, experiments were conducted to investigate the impact of parameter settings on both localization accuracy and runtime, and identify the voxel grid size of basis functions as the primary factor in geomagnetic map construction.

Key words: geomagnetic SLAM, multi-source fusion, inertial positioning, factor graph optimization, extended Kalman filter

中图分类号:

P232

邵克凡, 李增科, 孙猛, 刘振彬, 吴祺. 智能手机多源数据融合增强的地磁SLAM方法[J]. 测绘学报, 2025, 54(10): 1812-1825.

Kefan SHAO, Zengke LI, Meng SUN, Zhenbin LIU, Qi WU. A geomagnetic SLAM method enhanced by multi-source data fusion based on smartphones[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(10): 1812-1825.

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使用设备	不同SLAM方案	定位范围	状态估计方法
足部IMU	惯性SLAM^[10]	28 m×20 m	RBPF
智能手机	语义SLAM与机会相遇信号^[11]	3000 m²	粒子滤波与EKF
智能手机	视觉惯性里程计辅助的地磁SLAM^[13]	40 m×50 m	RBPF
足部IMU	地磁SLAM^[14]	15 m×20 m	EKF
智能手机	惯性SLAM与地磁SLAM^[15]	20 m×40 m	RBPF
智能手机	地磁SLAM与视觉SLAM^[17]	10 m×12 m	位姿图优化
智能平板	视觉惯性里程计辅助的地磁SLAM^[18]	15 m×20 m	EKF

手机型号	指标	方法1	方法2	方法3	方法4	方法5	方法6	方法7	方法8	方法9	方法10
小米8	RMSE	6.83	8.49	7.11	7.13	6.95	6.95	8.82	13.62	5.38	3.45
	MAXE	13.62	16.76	14.17	14.20	15.74	14.16	20.49	31.64	11.14	7.20
	MEANE	5.96	7.42	6.22	6.24	6.01	6.05	7.20	11.36	4.62	3.09
红米K30	RMSE	6.88	6.73	6.87	6.88	6.29	6.08	6.19	7.41	10.47	3.74
	MAXE	13.23	13.09	13.17	13.19	12.24	12.01	11.66	16.84	21.83	7.04
	MEANE	6.08	5.85	6.08	6.09	5.57	5.31	5.37	6.33	8.89	3.35

手机型号	指标	方法1	方法2	方法6	方法10
小米8	RMSE	3.58	3.54	3.87	2.87
	MAXE	8.11	6.93	6.61	4.91
	MEANE	3.10	3.26	3.51	2.65
红米K30	RMSE	3.26	3.12	2.95	2.07
	MAXE	5.84	6.41	6.15	6.66
	MEANE	2.91	2.79	2.36	1.62

方法	RMSE	MAXE	MEANE
8	3.10	5.67	2.80
10	2.87	4.91	2.65

智能手机多源数据融合增强的地磁SLAM方法

A geomagnetic SLAM method enhanced by multi-source data fusion based on smartphones

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