A Smart Helmet Framework Based on Visual-Inertial SLAM and Multi-Sensor Fusion to Improve Situational Awareness and Reduce Hazards in Mountaineering

A Smart Helmet Framework Based on Visual-Inertial SLAM and Multi-Sensor Fusion to Improve Situational Awareness and Reduce Hazards in Mountaineering

Charles Shi Tan
DOI: 10.4018/IJSSCI.333628
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Sensitivity to surrounding circumstances is essential for the safety of mountain scrambling. In this paper, the authors present a smart helmet prototype equipped with visual SLAM (simultaneous localization and mapping) and barometer multi-sensor fusion (MSF), IMU (inertial measurement unit), omnidirectional camera, and global navigation satellite system (GNSS). They equipped the helmet framework with SLAM to produce 3D semi-dense pointcloud environment maps, which are then discretized into grids. Then, the novel danger metrics they proposed were calculated for each grid based on surface normal analysis. The A* algorithm was applied to generate safe and reliable paths based on minimizing the danger score. This proposed helmet system demonstrated robust performance in mapping mountain environments and planning safe, efficient traversal paths for climbers navigating treacherous mountain landscapes.
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Throughout human history, helmets have played a crucial role in safeguarding the lives of individuals facing dangerous environments and challenges. From ancient civilizations to the present day, the evolution of helmet technology has been driven by a relentless pursuit of safety and protection. In the early days, helmets were forged from basic materials such as leather and bronze. Warriors and adventurers wore them to guard against head injuries in combat and dangerous terrain. As the centuries passed, helmet design and materials advanced, incorporating iron, steel, and carbon fiber. These improvements significantly enhanced protective capabilities, but the helmet is still primarily focused on protecting against physical impacts.

The rise in amateur mountaineers has increased the risk of fatal accidents, necessitating advanced safety equipment. In the 21st century, mountaineers face numerous environmental and navigational hazards, requiring protection beyond physical impacts. Helmets equipped with computer vision and guidance capabilities are essential, aiding in decision-making under extreme conditions and ensuring safer, less strenuous routes, especially in altitude sickness and hypothermia cases.

A principal method used in our helmet framework is Simultaneous Localization and Mapping (Durrant‐Whyte & Bailey, 2006; Engel et al., 2018), which is widely used to enable a robot or autonomous vehicle to construct a map of an unfamiliar environment while simultaneously recognizing its position in that environment. The recent advancement in 3D map reconstruction (Grisetti et al., 2010) and SLAM (Ebadi et al., 2022b) not enable robots to precisely positioning and make autonomous decisions in a scalable approach (Kohlbrecher et al., 2011) that can be applied to extreme environments.

This technique facilitates the helmet system in navigating through intricate and unexplored areas by continuously updating its spatial awareness. By utilizing Simultaneous Localization and Mapping for generating detailed 3D terrain mapping and recognizing cliffs, edges, and overhangs, scramblers could more effectively evaluate potential risks of falling or rockfall and choose paths that are less exposed.

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