基于光信标的井下视觉精确定位方法

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中图分类号：TD67 文献标志码：A

Abstract：Ultra Wide Band （UWB） positioning technology in coal mine underground environments faces limitations including multipath effects，sparse deployment of positioning base stations，and dependence on communication cables.These issues lead to poor positioning stability，low accuracy，and communication interruptions in disaster scenarios，failing to meet the autonomous navigation and positioning requirements of intelligent mobile equipment suchasunmanned vehicles，robots，andautomated mining devices.To address these problems，a precise underground visual positioning method based on optical beacons was proposed.Optical beacon groupswere deployed in pairs on both sides of the roadway，periodically transmiting light signals containing unique codes.Cameras mounted on intelligent mobile equipment captured images containing the optical beacon groups.Bydecoding the light signals in the images to obtain uniquecodes and querying the known coordinates oftheopticalbeacons accordingly，distances between the intelligentmobile equipmentandtheoptical beacons were calculated based on the pixel positions of the optical beacons in the imagesand their actual coordinates.The Perspective-n-Point （PnP）algorithm was used to solve for the three-dimensional coordinates and pose of the intelligent mobile equipment.Positioning experiments conducted in confined spaces and roadways showed thatthe methodachieved three-dimensional precise positioning with erors lessthan O.15 meters and highprecision pose perception with Euler angle errors less than 7° ，outperforming UWB positioning accuracy.Compared with UWB positioning， this method requires no wireless communication network support and realizes underground thre-dimensional positioning and pose perception entirely through machine vision. It is particularly suitable for application in complex electromagnetic environments and areas with high positioning accuracy requirements， such as mining working faces.

Key Words： precise underground positioning; visual positioning; autonomous navigation; optical beacon; three-dimensional positioning; pose perception; PnP algorithm

0 引言

随着煤炭开采向少人化和无人化方向发展，井下无人驾驶车辆、机器人、自动采掘设备等智能移动装备将成为推动煤矿智能化转型的关键驱动力[1-4]。（剩余10652字）