Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies
Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic he...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4834 |
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| author | Emmanuel A. Merchán-Cruz Samuel Moveh Oleksandr Pasha Reinis Tocelovskis Alexander Grakovski Alexander Krainyukov Nikita Ostrovenecs Ivans Gercevs Vladimirs Petrovs |
| author_facet | Emmanuel A. Merchán-Cruz Samuel Moveh Oleksandr Pasha Reinis Tocelovskis Alexander Grakovski Alexander Krainyukov Nikita Ostrovenecs Ivans Gercevs Vladimirs Petrovs |
| author_sort | Emmanuel A. Merchán-Cruz |
| collection | DOAJ |
| description | Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. |
| format | Article |
| id | doaj-art-6ea09c9e78b34b808561832aa3bb0ec7 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6ea09c9e78b34b808561832aa3bb0ec72025-08-20T03:36:30ZengMDPI AGSensors1424-82202025-08-012515483410.3390/s25154834Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled TechnologiesEmmanuel A. Merchán-Cruz0Samuel Moveh1Oleksandr Pasha2Reinis Tocelovskis3Alexander Grakovski4Alexander Krainyukov5Nikita Ostrovenecs6Ivans Gercevs7Vladimirs Petrovs8Engineering Faculty, Transport and Telecommunication Institute, Lauvas Iela 2, LV-1019 Riga, LatviaEngineering Faculty, Transport and Telecommunication Institute, Lauvas Iela 2, LV-1019 Riga, Latvia3D Engineering SIA, Mežu Iela 41-33, LV-3405 Liepaja, Latvia3D Engineering SIA, Mežu Iela 41-33, LV-3405 Liepaja, LatviaEngineering Faculty, Transport and Telecommunication Institute, Lauvas Iela 2, LV-1019 Riga, LatviaEngineering Faculty, Transport and Telecommunication Institute, Lauvas Iela 2, LV-1019 Riga, LatviaEngineering Faculty, Transport and Telecommunication Institute, Lauvas Iela 2, LV-1019 Riga, LatviaEngineering Faculty, Transport and Telecommunication Institute, Lauvas Iela 2, LV-1019 Riga, LatviaEngineering Faculty, Transport and Telecommunication Institute, Lauvas Iela 2, LV-1019 Riga, LatviaSmart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions.https://www.mdpi.com/1424-8220/25/15/4834VSLAMsmartsafetyhelmetvisionintegrated |
| spellingShingle | Emmanuel A. Merchán-Cruz Samuel Moveh Oleksandr Pasha Reinis Tocelovskis Alexander Grakovski Alexander Krainyukov Nikita Ostrovenecs Ivans Gercevs Vladimirs Petrovs Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies Sensors VSLAM smart safety helmet vision integrated |
| title | Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies |
| title_full | Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies |
| title_fullStr | Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies |
| title_full_unstemmed | Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies |
| title_short | Smart Safety Helmets with Integrated Vision Systems for Industrial Infrastructure Inspection: A Comprehensive Review of VSLAM-Enabled Technologies |
| title_sort | smart safety helmets with integrated vision systems for industrial infrastructure inspection a comprehensive review of vslam enabled technologies |
| topic | VSLAM smart safety helmet vision integrated |
| url | https://www.mdpi.com/1424-8220/25/15/4834 |
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