Recent Advance of Auto Navigation in Indoor Scenarios

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1015

Special Issue Editors


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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: indoor positioning algorithm; integrated navigation algorithm; multi-information fusion method
Special Issues, Collections and Topics in MDPI journals
GNSS Research Center, Wuhan University, Wuhan 430072, China
Interests: inertial navigation; magnetic positioning; pedestrian navigation; vehicle positioning; multi-source fusion positioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Location-based services (LBS) play a crucial role in supporting people's work, travel, factory production, and other social activities, becoming an indispensable part of transportation, the economy, and society. Central to LBS is positioning, a fundamental technology that underpins its functionality. In recent years, the Global Navigation Satellite System (GNSS) has matured significantly, offering reliable meter-level or even centimeter-level location services around the clock in open outdoor environments. However, GNSS is unsuitable for shielded environments such as tunnels and indoor spaces. To complement GNSS, indoor positioning technologies have rapidly developed, including UWB, Wi-Fi, BLE, visible light, vision, magnetic field, inertial navigation, etc. Despite these advancements, GNSS-denied environments present challenges due to the complex and diverse indoor space structures, where a single indoor positioning technology may struggle with issues such as low availability, poor accuracy, high system costs, and limited coverage. Consequently, achieving wide-area, high-precision indoor positioning at a low cost has become a prominent and urgent research focus in the field of navigation and positioning.

This Special Issue aims to introduce the latest breakthroughs in the theoretical research, technological innovation, and practical application of autonomous navigation in indoor scenarios and its future development prospects. Topics of interest include, but are not limited to, the following:

  • Inertial navigation;
  • Pedestrian, vehicle, bicycle, and drone dead reckoning;
  • UWB, ultrasound, Wi-Fi, BLE, LED, visual, and magnetic positioning;
  • Attitude and heading reference system;
  • Gyroscope, accelerometer, and magnetometer calibration;
  • Data-driven positioning method;
  • Simultaneous localization and mapping (SLAM);
  • Crowdsourcing-based mapping;
  • Optimal estimation methods such as Kalman filtering and graph optimization.

Dr. Wenchao Zhang
Dr. Jian Kuang
Guest Editors

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Keywords

  • indoor positioning
  • auto navigation
  • dead reckoning
  • matching positioning
  • multi-source fusion

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Published Papers (1 paper)

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24 pages, 26629 KiB  
Article
Optimization Model-Based Robust Method and Performance Evaluation of GNSS/INS Integrated Navigation for Urban Scenes
by Dashuai Chai, Shijie Song, Kunlin Wang, Jingxue Bi, Yunlong Zhang, Yipeng Ning and Ruijie Yan
Electronics 2025, 14(4), 660; https://doi.org/10.3390/electronics14040660 - 8 Feb 2025
Viewed by 603
Abstract
The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal [...] Read more.
The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal interruptions in occluded environments, which lead to unreliable parameter estimations in GNSS/INS based on filter models. To address this issue, in this paper, a GNSS/INS combination model based on factor graph optimization (FGO) is investigated and the robustness of this optimization model is evaluated in comparison to the traditional extended Kalman filter (EKF) model and robust Kalman filter (RKF) model. In this paper, both high- and low-accuracy GNSS/INS combination data are used and the two sets of urban scene data are collected using high- and low-precision consumer-grade inertial guidance systems and an in-vehicle setup. The experimental results demonstrate that the position, velocity, and attitude estimates obtained using the GNSS/INS and the FGO model are superior to those obtained using the traditional EKF and robust EKF methods. In the simulated scenarios involving gross interference and GNSS signal loss, the FGO model achieves optimal results. The maximum improvement rates of the position, velocity, and attitude estimates are 81.1%, 73.8%, and 75.1% compared to the EKF method and 79.8%, 72.1%, and 57.1% compared to the RKF method, respectively. Full article
(This article belongs to the Special Issue Recent Advance of Auto Navigation in Indoor Scenarios)
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