MineBL: A Battery-Free Localization Scheme with Binocular Camera for Coal Mine

Abstract

Accurate localization in underground coal mining is a challenging technology in coal mine safety production. This paper proposes a low-cost battery-free localization scheme based on depth images, called MineBL. The main idea is to utilize the battery-free low-cost reflective balls as position nodes and realize underground target localization with a series of algorithms. In particular, the paper designs a data enhancement strategy based on small-target reorganization to increase the identification accuracy of tiny position nodes. Moreover, a novel ranging algorithm based on multi-filter cooperative denoising has been proposed, and an optimized weighted centroid location algorithm based on multilateral location errors has been designed to minimize underground localization errors. Many experiments in the indoor laboratories and the underground coal mine laboratories have been conducted, and the experimental results have verified that MineBL has good localization performances, with localization errors less than 30 cm in 95% of cases. Therefore, MineBL has great potential to provide a low-cost and effective solution for precise target localization in complex underground environments.

The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines

“…A low-cost battery-free localization scheme called MineBL was proposed [99] for accurate positioning in underground coal mines. The approach utilizes reflective balls as position nodes and applies a series of algorithms based on depth images to achieve target localization.…”

Localization for Autonomous Vehicles in Underground Mines

“…Other camera based systems have been suggested for the underground application where the camera identifies some kind of markers placed in the mine with known absolute positions. Passive, reflective balls are used in [118] and in [149] barcode tags are placed on the tunnel walls. Low illumination, which is sometimes the case in underground mines, poses special challenges for vision based systems, and in [149] the vision based system is fused with an ultrasonic sensor measuring distances to the walls.…”

Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things

“…These approaches use the exact location of nearby anchors to enable unknown nodes to calculate their locations within WSNs. Examples of range-free approaches include the DV-Hop algorithm, APIT technique, Centroid algorithm, and Amorphous [23][24][25][26]. APIT [24] calculates the locations of sensor nodes using the positions of a minimum of three anchor nodes.…”

“…The algorithm obtains improved performance with a high percentage of anchors. The Centroid method [25] considers the barycenter of nearby anchors as the determined positions of nodes, avoiding the need for additional materials. Amorphous [26] is similar to DV-Hop in that it accurately determines the locations of unknown nodes.…”