Abstract
Underground personnel localization is highly important in the operations of coal mines. Considering the special underground environment, this paper introduces a novel localization scheme based on step detection and image recognition technologies, which makes use of unique characteristics of the underground environment like the dark environment and the miner’s lamp. Since the underground topology is relatively simple, the miner can be located only by step information. However, the localization with step information always causes the problem of cumulative error. To solve this problem, we rebuild a special base station with a camera in a dark underground environment. A miner’s lamp, which every miner carries, can simply transform to irradiate unique shapes (such as triangles, rectangles, and circles) and every coal miner at the base station can identify these shapes based on image recognition technologies. Thus, we can obtain the miner’s precise position when he/she is passing by a base station. In that way, we can correct the localization results to solve cumulative error. We implemented our algorithm in indoor and underground environments. The experimental results show that 96% of spatial errors were 2.5 m or less.
Highlights
Underground personnel real-time positions are important for relief efforts when a mine accident occurs [1]
Extensive approaches which are based on smartphone are springing up to deal with the problem of indoor localization.At the same time, a large amount of candidates appear to take the place of those require specific hardware for achieving the same goal, like infrared [16,17], ultrasonic, RFID [18,19,20] and Zigbee [21], and they are classified based on tag
Coal miners can be accurately positioned when they pass by the base station by identifying the shape of the image captured by the camera
Summary
Underground personnel real-time positions are important for relief efforts when a mine accident occurs [1]. Coal miners can be accurately located whenever they pass by the base station, thereby solving the cumulative error problem of a step-based localization algorithm. We use the step and stride-length to locate the coal miner This localization method is not accurate due to the cumulative error problem. The proposed localization scheme consists of four phases: (1) construction of base station, (2) detection of step, (3) calculation of stride-length, and (4) real-time localization. To the best of our knowledge, this is a novel image-assisted step-based human localization scheme for mines, which uses the step detection and image recognition techniques It can achieve better positioning accuracy with low-cost. We design a novel image-assisted method to improve the cumulative error problem of step-based localization scheme. Part 5 concludes the paper and provides future work directions
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