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Indoor Mobile Mapping and Location-Based Knowledge Services

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A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: 30 June 2026 | Viewed by 22840

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Special Issue Editors

Prof. Dr. Eliseo Clementini

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Department of Industrial and Information Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy
Interests: spatial databases; spatial query languages; mathematical modeling of spatial information; computational geometry; spatio-temporal reasoning; wualitative modeling of geographical information; indoor and outdoor navigation; volunteered geographic information
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Dr. Zhiyong Zhou

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Department of Geography, University of Zurich, Zurich, Switzerland
Interests: indoor and outdoor mobility analytics; geospatial data engineering; geospatial information communication; GeoAI applications for sustainable cities

Special Issue Information

Dear Colleagues,

The ability to accurately map and navigate indoor environments is a cornerstone of modern spatial technologies, driving innovation in diverse fields such as smart cities, healthcare, logistics, and augmented reality. Unlike outdoor environments, where GPS and satellite imaging dominate, indoor spaces present unique challenges: signal occlusion, a lack of global positioning system (GPS) coverage, and complex spatial structures. These challenges have spurred the development of advanced indoor mobile mapping technologies and location-based knowledge services that leverage novel sensors, algorithms, and data processing techniques.

Indoor mobile mapping combines tools like LiDAR, vision systems, and sensor fusion to create high-resolution, three-dimensional representations of spaces. Paired with sophisticated localization methods such as Wi-Fi fingerprinting, ultra-wideband (UWB) tracking, or visual-inertial odometry; these systems enable precise navigation and spatial analysis within buildings. As the amount of spatial data increases, the focus is shifting from mere data collection to actionable insights, with location-based knowledge services playing a pivotal role in transforming raw spatial information into user-centric solutions for navigation, resource optimization, and decision-making.

The importance of this research area is underscored by its applicability across numerous industries:

Smart Cities and Infrastructure: Enabling efficient building management and energy optimization.
Retail and Logistics: Facilitating real-time inventory tracking and customer navigation.
Healthcare: Supporting indoor navigation in hospitals and resource allocation during emergencies.
Emergency Response: Enhancing rescue operations with real-time indoor positioning.

This Special Issue aims to achieve the following:

Highlight Advances in Technology: Showcase the latest breakthroughs in indoor mapping and localization technologies, including sensor development, algorithmic innovations, and system integrations.
Foster Interdisciplinary Research: Bring together expertise from geospatial science, computer vision, robotics, and data analytics to address the multifaceted challenges of indoor environments.
Promote Applications and Case Studies: Provide a platform for real-world implementations of indoor mapping and location-based knowledge services, demonstrating their value across various sectors.
Explore Standards and Ethical Considerations: Address the need for interoperability, accuracy benchmarks, and privacy-preserving methodologies to guide the responsible development of these technologies.
Define Future Directions: Identify research gaps and emerging opportunities to inspire innovation and collaboration within this growing field.

Relevance to the Journal’s Scope

The subject of this Special Issue aligns closely with the journal’s focus on advancing spatial sciences, technology development, and applications. Indoor mobile mapping and location-based knowledge services embody the interdisciplinary nature of the journal’s scope by integrating geospatial technologies, data science, and practical applications. Furthermore, the topics proposed resonate with the journal's commitment to fostering innovation that benefits society and supports sustainable, efficient, and intelligent systems.

Themes for Exploration

This Special Issue would like to invite contributions on the following themes:

Advanced sensors and algorithms for indoor mapping.
AI-driven approaches for indoor localization and mapping.
Integration of indoor and outdoor mapping systems.
Applications of AR/VR in location-based knowledge services.
Standards and benchmarks for indoor spatial data.
Privacy-preserving solutions for indoor LBS.
Case studies on successful deployments in diverse industries.
Evacuation simulation with real-time positioning.

By addressing these motivations and themes, this Special Issue aims to provide a comprehensive understanding of current advancements, challenges, and future opportunities in indoor mobile mapping and location-based knowledge services, setting the stage for impactful research and innovation.

Prof. Dr. Eliseo Clementini
Dr. Zhiyong Zhou
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1900 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.

Keywords

indoor mapping technologies
location-based knowledge services
indoor navigation systems
geospatial data for indoor spaces
real-time positioning and tracking
AI-driven indoor location services
indoor spatial analytics
smart indoor environments

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Published Papers (11 papers)

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24 pages, 12711 KB

Open AccessArticle

Evidentially Driven Uncertainty Decomposition for Weakly Supervised Point Cloud Semantic Segmentation

by Qingyan Wang, Yixin Wang, Junping Zhang, Yujing Wang and Shouqiang Kang

ISPRS Int. J. Geo-Inf. 2026, 15(4), 167; https://doi.org/10.3390/ijgi15040167 - 12 Apr 2026

Viewed by 174

Abstract

Point cloud semantic segmentation is a core component in indoor scene understanding and autonomous driving. Under weak point-level supervision, only a small subset of points is annotated, making effective use of unlabeled points critical yet non-trivial. Many existing approaches rely on prediction confidence to filter pseudo labels or enforce consistency, which can bias training toward easy points and amplify early mistakes. Consequently, confidently wrong predictions may be reinforced, while uncertain points around class boundaries or in geometrically complex regions are less utilized, limiting further gains. An evidential uncertainty decomposition framework is introduced for weakly supervised point cloud semantic segmentation. Network outputs are interpreted as evidential distributions, and uncertainty is decomposed to separate lack-of-knowledge uncertainty from boundary-related ambiguity, providing a more informative reliability signal for unlabeled points. Based on this signal, different constraints are applied to different subsets: reliable points are trained with pseudo labels together with prototype-based regularization to encourage intra-class compactness; boundary-ambiguous points are guided by evidential consistency to improve boundary learning; and points with high epistemic uncertainty are excluded from pseudo-label-based supervision to mitigate error reinforcement. In addition, an uncertainty calibration term on sparsely labeled points helps stabilize training. Experiments on S3DIS, ScanNet-V2, and SemanticKITTI yield 67.7%, 59.7%, and 53.3% mIoU, respectively, with only 0.1% labeled points, comparing favorably with prior weakly supervised point cloud segmentation methods. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

26 pages, 4960 KB

Open AccessArticle

TGR-T: Truncated-Gaussian-Weighted Reliability for Adaptive Dynamic Thresholding in Weakly Supervised Indoor 3D Point Cloud Segmentation

by Ziwei Luo, Xinyue Liu, Jun Jiang, Hanyu Qi, Chen Wang, Zhong Xie and Tao Zeng

ISPRS Int. J. Geo-Inf. 2026, 15(3), 108; https://doi.org/10.3390/ijgi15030108 - 4 Mar 2026

Viewed by 352

Abstract

Indoor 3D point cloud semantic segmentation is a fundamental task for fine-grained scene understanding and intelligent perception. Due to the prohibitive cost of dense point-wise annotations, weakly supervised learning has emerged as a promising alternative for indoor point cloud segmentation. However, existing weakly supervised methods commonly rely on fixed confidence thresholds for pseudo-label selection, which exhibit limited generalization caused by threshold sensitivity, underutilization of informative low-confidence regions, and progressive noise accumulation during self-training. To address these issues, we propose TGR-T, a weakly supervised framework for indoor 3D point cloud semantic segmentation that incorporates truncated-Gaussian-weighted reliability with adaptive dynamic thresholding. Specifically, a reliability-adaptive dynamic thresholding strategy is introduced to guide pseudo-label selection based on the evolving confidence statistics of unlabeled mini-batches, with exponential moving average smoothing employed to produce stable global estimates and robust separation of reliable and ambiguous regions. To further exploit uncertain regions, a learnable truncated Gaussian weighting function is designed to explicitly model prediction uncertainty within the ambiguous set, providing soft supervision by assigning adaptive weights to low-confidence predictions during optimization. Extensive experimental results demonstrate that the proposed framework significantly enhances the exploitation of unlabeled data under extremely limited supervision: extensive experiments conducted on standard indoor 3D scene benchmarks demonstrate that TGR-T achieves competitive or superior segmentation performance under extremely sparse supervision and can even outperform several fully supervised baselines trained with dense annotations while using only 1% labeled points, thereby substantially narrowing the performance gap between weakly supervised and fully supervised 3D semantic segmentation methods. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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26 pages, 4670 KB

Open AccessArticle

Construction of Ultra-Wideband Virtual Reference Station and Research on High-Precision Indoor Trustworthy Positioning Method

by Yinzhi Zhao, Jingui Zou, Bing Xie, Jingwen Wu, Zhennan Zhou and Gege Huang

ISPRS Int. J. Geo-Inf. 2026, 15(1), 50; https://doi.org/10.3390/ijgi15010050 - 22 Jan 2026

Viewed by 572

Abstract

With the development of the Internet of Things (IoT) and smart industry, the demand for high-precision indoor positioning is becoming increasingly urgent. Ultra-ideband (UWB) technology has become a research hotspot due to its centimeter-level ranging accuracy, good penetration, and high multipath resolution. However, in complex environments, it still faces problems such as high cost of anchor node layout, gross errors in observation data, and difficulty in eliminating systematic errors such as electronic time delay. To address the aforementioned problems, this paper proposes a comprehensive UWB indoor positioning scheme. By constructing virtual reference stations to enhance the observation network, the geometric structure is optimized and the dependence on physical anchors is reduced. Combined with a gross error elimination method under short-baseline constraints and a double-difference positioning model including virtual observations, it systematically suppresses systematic errors such as electronic delay. Additionally, a quality control strategy with velocity constraints is introduced to improve trajectory smoothness and reliability. Static experimental results show that the proposed double-difference model can effectively eliminate systematic errors. For example, the positioning deviation in the Xdirection is reduced from approximately 2.88 cm to 0.84 cm, while the positioning accuracy in the Ydirection slightly decreases. Overall, the positioning accuracy is improved. The gross error elimination method achieves an identification efficiency of over 85% and an accuracy of higher than 99%, providing high-quality observation data for subsequent calculations. Dynamic experimental results show that the positioning trajectory after geometric enhancement of virtual reference stations and velocity-constrained quality control is highly consistent with the reference trajectory, with significantly improved trajectory smoothness and reliability. In summary, this study constructs a complete technical chain from data preprocessing to result quality control, effectively improving the accuracy and robustness of UWB positioning in complex indoor environments, and exhibits promising engineering application potential. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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30 pages, 5730 KB

Open AccessArticle

Indoor UAV 3D Localization Using 5G CSI Fingerprinting

by Mohsen Shahraki, Ahmed Elamin and Ahmed El-Rabbany

ISPRS Int. J. Geo-Inf. 2026, 15(1), 24; https://doi.org/10.3390/ijgi15010024 - 5 Jan 2026

Viewed by 923

Abstract

Fifth-generation (5G) wireless networks have been widely deployed across various applications, including indoor positioning. This paper presents a model for 3D indoor localization of an unmanned aerial vehicle (UAV) using 5G millimeter-wave technology. Wireless InSite software is used to simulate a real-world environment and extract channel state information from multiple 5G next-generation NodeBs (gNBs), which is then used to generate channel frequency response (CFR) images. These images are employed in a fingerprinting method, where a deep convolutional neural network is trained for accurate position prediction. The model is trained across multiple scenarios involving changes in the number of gNBs, receiver positions, and spacing. In all scenarios, the model is tested using a UAV flying along a trajectory at variable speed. It is shown that a mean positioning error (MPE) of 0.36 m in 2D and 0.43 m in 3D is achieved when twelve gNBs with receivers spaced at 0.25 m are used. In addition, the corresponding root mean square error (RMSE) values of 0.32 m (2D) and 0.33 m (3D) further confirm the stability of the localization performance by indicating a low dispersion of positioning errors. This demonstrates that high positioning accuracy is feasible, even when synchronization errors and hardware imperfections exist. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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22 pages, 3966 KB

Open AccessEditor’s ChoiceArticle

TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation

by Yiming Li, Liuwei Lu, Guangming Guo, Luying Na, Xianpu Liang, Peng Su, Qi An and Pengjiang Wang

ISPRS Int. J. Geo-Inf. 2026, 15(1), 7; https://doi.org/10.3390/ijgi15010007 - 21 Dec 2025

Viewed by 752

Abstract

To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes—leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM method integrating instance-level motion classification, temporally adaptive super-pixel segmentation, and optical flow propagation. The system first employs an instance-level motion classifier combining residual flow estimation and a YOLOv8-seg instance segmentation model to distinguish moving objects. Then, temporally adaptive super-pixel segmentation algorithm SLIC (TA-SLIC) is applied to achieve fine-grained dynamic region partitioning. Subsequently, a proposed dynamic region missed-detection correction mechanism based on optical flow propagation (OFP) is used to refine the missed-detection mask, enabling accurate identification and capture of motion regions containing non-rigid local object movements, undefined moving objects, and low-dynamic objects. Finally, dynamic feature points are removed, and valid static features are utilized for pose estimation. The localization accuracy of the visual SLAM system is validated using two widely adopted datasets, TUM and BONN. Experimental results demonstrate that the proposed method effectively suppresses interference from dynamic objects (particularly non-rigid local motions) and significantly enhances both localization accuracy and system robustness in dynamic environments. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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17 pages, 5641 KB

Open AccessArticle

A Novel Smartphone PDR Framework Based on Map-Aided Adaptive Particle Filter with a Reduced State Space

by Mengchi Ai, Ilyar Asl Sabbaghian Hokmabadi and Xuan Zhao

ISPRS Int. J. Geo-Inf. 2025, 14(12), 476; https://doi.org/10.3390/ijgi14120476 - 2 Dec 2025

Viewed by 2499

Abstract

Accurate, reliable and infrastructure-free indoor positioning using a smartphone is considered an essential topic for applications such as indoor emergency response and indoor path planning. While the inertial measurement units (IMU) offer continuous and high-frequency motion data, pedestrian dead reckoning (PDR) based on IMU data suffers from significant and accumulative errors. Map-aided particle filters (PFs) are important pose estimation frameworks that have exhibited capabilities to eliminate drifts by incorporating additional constraints from a pre-built floor map, without relying on other wireless or perception-based infrastructures. However, despite the recent approaches, a key challenging issue remains: existing map-aided PF-PDR solutions are computationally demanding, as they typically rely on a large number of particles and require map boundaries to eliminate non-matching particles. This process introduces substantial computational overhead, limiting efficiency and real-time performance on resource-constrained platforms such as smartphones. To address this key issue, this work proposes a novel map-aided PF-PDR framework that leverages a smartphone’s IMU data and a pre-built vectorized floor plan map. The proposed method introduces an adaptive PF-PDR solution that detects particle convergence using a cross-entropy distance of the particles and a Gaussian distribution. The number of particles is reduced significantly after a convergence is detected. Further, in order to reduce the computational cost, only the heading is included in particle attitude sampling. The heading is estimated accurately by levelling gyroscope measurements to a virtual plane, parallel to the ground. Experiments are performed using a dataset collected on a smartphone and the results demonstrate improved performance, especially in drift reduction, achieving an mean position error of 0.9 m and a processing rate of 37.0 Hz. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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21 pages, 2429 KB

Open AccessEditor’s ChoiceArticle

Visualizing Spatial Cognition for Wayfinding Design: Examining Gaze Behaviors Using Mobile Eye Tracking in Counseling Service Settings

by Jain Kwon, Alea Schmidt, Chenyi Luo, Eunwoo Jun and Karina Martinez

ISPRS Int. J. Geo-Inf. 2025, 14(10), 406; https://doi.org/10.3390/ijgi14100406 - 16 Oct 2025

Cited by 4 | Viewed by 3541

Abstract

Wayfinding with minimal effort is essential for reducing cognitive load and emotional stress in unfamiliar environments. This exploratory quasi-experimental study investigated wayfinding challenges in a university building housing three spatially dispersed counseling centers and three academic departments that share the building entrances, lobby, and hallways. Using mobile eye tracking with concurrent think-aloud protocols and schematic mapping, we examined visual attention patterns during predefined navigation tasks performed by 24 first-time visitors. Findings revealed frequent fixations on non-informative structural features, while existing wayfinding cues were often overlooked. High rates of null gazes indicated unsuccessful visual searching. Thematic analysis of verbal data identified eight key issues, including spatial confusion, aesthetic monotony, and inadequate signage. Participants frequently described the environment as disorienting and emotionally taxing, comparing it to institutional settings such as hospitals. In response, we developed wayfinding design proposals informed by our research findings, stakeholder needs, and contextual priorities. We used an experiential digital twin that prioritized perceptual fidelity to analyze the current wayfinding challenges, develop experimental protocols, and discuss design options and costs. This study offers a transferable methodological framework for identifying wayfinding challenges through convergent analysis of gaze patterns and verbal protocols, demonstrating how empirical findings can inform targeted wayfinding design interventions. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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26 pages, 3522 KB

Open AccessArticle

PCA-GWO-KELM Optimization Gait Recognition Indoor Fusion Localization Method

by Xiaoyu Ji, Xiaoyue Xu, Suqing Yan, Jianming Xiao, Qiang Fu and Kamarul Hawari Bin Ghazali

ISPRS Int. J. Geo-Inf. 2025, 14(7), 246; https://doi.org/10.3390/ijgi14070246 - 26 Jun 2025

Viewed by 3517

Abstract

Location-based services have important economic and social values. The positioning accuracy and cost have a crucial impact on the quality, promotion, and market competitiveness of location services. Dead reckoning can provide accurate location information in a short time. However, it suffers from motion pattern diversity and cumulative error. To address these issues, we propose a PCA-GWO-KELM optimization gait recognition indoor fusion localization method. In this method, 30-dimensional motion features for different motion patterns are extracted from inertial measurement units. Then, constructing PCA-GWO-KELM optimization gait recognition algorithms to obtain important features, the model parameters of the kernel-limit learning machine are optimized by the gray wolf optimization algorithm. Meanwhile, adaptive upper thresholds and adaptive dynamic time thresholds are constructed to void pseudo peaks and valleys. Finally, fusion localization is achieved by combining with acoustic localization. Comprehensive experiments have been conducted using different devices in two different scenarios. Experimental results demonstrated that the proposed method can effectively recognize motion patterns and mitigate cumulative error. It achieves higher localization performance and universality than state-of-the-art methods. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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25 pages, 9860 KB

Open AccessArticle

Indoor Dynamic Environment Mapping Based on Semantic Fusion and Hierarchical Filtering

by Yiming Li, Luying Na, Xianpu Liang and Qi An

ISPRS Int. J. Geo-Inf. 2025, 14(7), 236; https://doi.org/10.3390/ijgi14070236 - 21 Jun 2025

Viewed by 1701

Abstract

To address the challenges of dynamic object interference and redundant information representation in map construction for indoor dynamic environments, this paper proposes an indoor dynamic environment mapping method based on semantic fusion and hierarchical filtering. First, prior dynamic object masks are obtained using the YOLOv8 model, and geometric constraints between prior static objects and dynamic regions are introduced to identify non-prior dynamic objects, thereby eliminating all dynamic features (both prior and non-prior). Second, an initial semantic point cloud map is constructed by integrating prior static features from a semantic segmentation network with pose estimates from an RGB-D camera. Dynamic noise is then removed using statistical outlier removal (SOR) filtering, while voxel filtering optimizes point cloud density, generating a compact yet texture-rich semantic dense point cloud map with minimal dynamic artifacts. Subsequently, a multi-resolution semantic octree map is built using a recursive spatial partitioning algorithm. Finally, point cloud poses are corrected via Transform Frame (TF) transformation, and a 2D traversability grid map is generated using passthrough filtering and grid projection. Experimental results demonstrate that the proposed method constructs multi-level semantic maps with rich information, clear structure, and high reliability in indoor dynamic scenarios. Additionally, the map file size is compressed by 50–80%, significantly enhancing the reliability of mobile robot navigation and the efficiency of path planning. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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21 pages, 6514 KB

Open AccessEditor’s ChoiceArticle

Evacuation Behavioural Instructions with 3D Motions: Insights from Three Use Cases

by Ruihang Xie, Sisi Zlatanova, Jinwoo (Brian) Lee and André Borrmann

ISPRS Int. J. Geo-Inf. 2025, 14(5), 197; https://doi.org/10.3390/ijgi14050197 - 8 May 2025

Cited by 2 | Viewed by 3046

Abstract

During emergency evacuations, pedestrians may use three-dimensional (3D) motions, such as low crawling and climbing up/down, to navigate above or below indoor objects (e.g., tables, chairs, and stair flights). Understanding how these motions influence evacuation processes can facilitate the development of behavioural instructions. This study examines the influence of 3D motions through a simulation-based method. This method combines a voxel-based 3D indoor model with an agent-based model. Three use case studies are elaborated upon, considering varying building types, agent numbers, urgency levels, and demographic differences. These case studies serve as exploratory demonstrations rather than validated simulations grounded in real-world evacuation experiments. Our findings are as follows: (1) Three-dimensional motions may create alternative and local 3D paths, enabling agents to bypass congestion, particularly in narrow corridors and confined spaces. (2) While 3D motions may help alleviate local congestion, they may intensify bottlenecks near exits, especially in highly crowded and high-urgency scenarios. (3) As urgency and agent numbers increase, differences in evacuation efficiency between scenarios with and without 3D motions are likely to diminish. We suggest further investigation into evacuation behavioural instructions, including the following: (1) conditional use of 3D motions in different buildings and (2) instructions tailored to different demographic groups. These use cases illustrate new directions for evacuation managers to consider the incorporation of 3D motions. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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23 pages, 7181 KB

Open AccessTechnical Note

Nav-YOLO: A Lightweight and Efficient Object Detection Model for Real-Time Indoor Navigation on Mobile Platforms

by Cheng Su, Litao Zhu, Wen Dai, Jin Zhou, Jialiang Wang, Yucheng Mao and Jiangbing Sun

ISPRS Int. J. Geo-Inf. 2025, 14(9), 364; https://doi.org/10.3390/ijgi14090364 - 19 Sep 2025

Cited by 2 | Viewed by 3544

Abstract

Precise object detection is fundamental to robust indoor navigation and localization. However, the practical deployment of deep learning-based detectors on mobile platforms is frequently impeded by their extensive parameter counts, substantial computational overhead, and prolonged inference latency, rendering them impractical for real-time and GPU-independent applications. To overcome these limitations, this paper presents Nav-YOLO, a highly optimized and lightweight architecture derived from YOLOv8n, specifically engineered for navigational tasks. The model’s efficiency stems from several key improvements: a ShuffleNetv2-based backbone significantly reduces model parameters; a Slim-Neck structure incorporating GSConv and GSbottleneck modules streamlines the feature fusion process; the VoV-GSCSP hierarchical network aggregates features with minimal computational cost; and a compact detection head is designed using Hybrid Convolutional Transformer Architecture Search (HyCTAS). Furthermore, the adoption of Inner-IoU as the bounding box regression loss accelerates the convergence of the training process. The model’s efficacy is demonstrated through a purpose-built Android application. Experimental evaluations on the VOC2007 and VOC2012 datasets reveal that Nav-YOLO substantially outperforms the baseline YOLOv8n, achieving mAP50 improvements of 10.3% and 5.0%, respectively, while maintaining a comparable parameter footprint. Consequently, Nav-YOLO demonstrates a superior balance of accuracy, model compactness, and inference speed, presenting a compelling alternative to existing object detection algorithms for mobile systems. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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