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Multi-sensor Integration for Navigation and Environmental Sensing
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Multi-sensor Integration for Navigation and Environmental Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 5569

Special Issue Editor

Prof. Dr. Ahmed El-Rabbany

E-Mail Website
Guest Editor
Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
Interests: satellite navigation; multi-sensor integration; mobile sensing; unmanned aerial systems

Special Issue Information

Dear Colleagues,

The evolution of navigation and perception sensor technologies in recent years has opened the door for developing low-cost autonomous systems, including autonomous vehicles and small unmanned aerial systems (UAS). Such systems can potentially be used in many critical applications, both outdoor and indoor. Outdoor applications include mobile mapping, disaster monitoring, and precision agriculture, to name a few. Indoor applications, on the other hand, include manufacturing and distribution facilities, the health sector, and public safety, among others. The use of autonomous systems in these critical applications, however, requires continuous high-accuracy positioning and attitude (pose) information of the platform, along with environmental sensing and object classification. In addition, some use cases of market verticals such as smart manufacturing, warehousing and supply chains, and transportation require seamless indoor/outdoor operational capabilities.

MDPI is planning to publish a special issue of its Sensors journal on multi-sensor integration for navigation and environmental sensing. This issue will focus on next-generation algorithms and estimation methodologies for a low-cost autonomous multi-sensor integrated system for precise real-time seamless indoor/outdoor navigation and environmental sensing. Topics of particular interest include but are not limited to:

  1. Multi-sensor integration for mobile and UAS mapping;
  2. Integration of monocular/stereo/event camera-based visual-inertial odometry (VIO)/simultaneous localization and mapping (SLAM);
  3. Integration of solid-state LiDAR-inertial odometry (LIO)/SLAM;
  4. Real-time autonomous tightly-coupled GNSS/LVIO integration for challenging GNSS signal, weather, and illumination conditions;
  5. Integration of fifth-generation (5G) millimeter wave (mmWave)/LVIO for GNSS-denied environments;
  6.  Deep-learning-based algorithms for classification and semantic segmentation of LiDAR/Photogrammetric point cloud of the surrounding environment.

Prof. Dr. Ahmed El-Rabbany
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 4607 KiB  
Article
Event-Based Visual/Inertial Odometry for UAV Indoor Navigation
by Ahmed Elamin, Ahmed El-Rabbany and Sunil Jacob
Sensors 2025, 25(1), 61; https://doi.org/10.3390/s25010061 - 25 Dec 2024
Cited by 2 | Viewed by 1709
Abstract
Indoor navigation is becoming increasingly essential for multiple applications. It is complex and challenging due to dynamic scenes, limited space, and, more importantly, the unavailability of global navigation satellite system (GNSS) signals. Recently, new sensors have emerged, namely event cameras, which show great [...] Read more.
Indoor navigation is becoming increasingly essential for multiple applications. It is complex and challenging due to dynamic scenes, limited space, and, more importantly, the unavailability of global navigation satellite system (GNSS) signals. Recently, new sensors have emerged, namely event cameras, which show great potential for indoor navigation due to their high dynamic range and low latency. In this study, an event-based visual–inertial odometry approach is proposed, emphasizing adaptive event accumulation and selective keyframe updates to reduce computational overhead. The proposed approach fuses events, standard frames, and inertial measurements for precise indoor navigation. Features are detected and tracked on the standard images. The events are accumulated into frames and used to track the features between the standard frames. Subsequently, the IMU measurements and the feature tracks are fused to continuously estimate the sensor states. The proposed approach is evaluated using both simulated and real-world datasets. Compared with the state-of-the-art U-SLAM algorithm, our approach achieves a substantial reduction in the mean positional error and RMSE in simulated environments, showing up to 50% and 47% reductions along the x- and y-axes, respectively. The approach achieves 5–10 ms latency per event batch and 10–20 ms for frame updates, demonstrating real-time performance on resource-constrained platforms. These results underscore the potential of our approach as a robust solution for real-world UAV indoor navigation scenarios. Full article
(This article belongs to the Special Issue Multi-sensor Integration for Navigation and Environmental Sensing)
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23 pages, 5731 KiB  
Article
A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors
by Huaikun Gao, Xu Li and Xiang Song
Sensors 2024, 24(2), 476; https://doi.org/10.3390/s24020476 - 12 Jan 2024
Cited by 4 | Viewed by 1505
Abstract
For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision [...] Read more.
For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA–GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy. Full article
(This article belongs to the Special Issue Multi-sensor Integration for Navigation and Environmental Sensing)
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17 pages, 7617 KiB  
Article
A Robust GPS Navigation Filter Based on Maximum Correntropy Criterion with Adaptive Kernel Bandwidth
by Dah-Jing Jwo, Yi-Ling Chen, Ta-Shun Cho and Amita Biswal
Sensors 2023, 23(23), 9386; https://doi.org/10.3390/s23239386 - 24 Nov 2023
Cited by 4 | Viewed by 1565
Abstract
Multiple forms of interference and noise that impact the receiver’s capacity to receive and interpret satellite signals, and consequently the preciseness of positioning and navigation, may be present during the processing of Global Positioning System (GPS) navigation. The non-Gaussian noise predominates in the [...] Read more.
Multiple forms of interference and noise that impact the receiver’s capacity to receive and interpret satellite signals, and consequently the preciseness of positioning and navigation, may be present during the processing of Global Positioning System (GPS) navigation. The non-Gaussian noise predominates in the signal owing to the fluctuating character of both natural and artificial electromagnetic interference, and the algorithm based on the minimum mean-square error (MMSE) criterion performs well when assuming Gaussian noise, but drops when assuming non-Gaussian noise. The maximum correntropy criteria (MCC) adaptive filtering technique efficiently reduces pulse noise and has adequate performance in heavy-tailed noise, which addresses the issue of filter performance caused by the presence of non-Gaussian or heavy-tailed unusual noise values in the localizing measurement noise. The adaptive kernel bandwidth (AKB) technique employed in this paper applies the calculated adaptive variables to generate the kernel function matrix, in which the adaptive factor can modify the size of the kernel width across a reasonably appropriate spectrum, substituting the fixed kernel width for the conventional MCC to enhance the performance. The conventional maximum correntropy criterion-based extended Kalman filter (MCCEKF) algorithm’s performance is significantly impacted by the value of the kernel width, and there are certain predetermined conditions in the selection based on experience. The MCCEKF with a fixed adaptive kernel bandwidth (MCCEKF-AKB) has several advantages due to its novel concept and computational simplicity, and gives a qualitative solution for the study of random structures for generalized noise. Additionally, it can effectively achieve the robust state estimation of outliers with anomalous values while guaranteeing the accuracy of the filtering. Full article
(This article belongs to the Special Issue Multi-sensor Integration for Navigation and Environmental Sensing)
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