# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 1 Introduction  
With the rapid development of the industrial Internet, big data, and artificial intelligence, the intellectualization and robotization of equipment have become an inevitable trend in industrial development. Based on literature data from the Web of Science database, a cluster analysis of hotspots in intelligent coal mine research, from 2010 to 2020, obtained using VOSviewer software, is shown in Figure 1. It reveals that the in-depth integration of intelligent technology and the development of the coal industry is inevitable. For China's coal industry, the degree of automation and intelligence of fully mechanized mining faces has been significantly improved over years of innovative development, and has been applied practically in many mining areas [1, 2]. However, the intelligent development of fully mechanized excavation faces is still in a bottleneck period due to the poor working environments and complex excavation processes; the imbalance between mining and tunneling seriously restricts the safety and efficiency of coal mine production. Therefore, it is urgent to carry out research on the intelligence of tunneling equipment and systems for a fully mechanized excavation face [3, 4]. As the main equipment for coal roadway driving, autonomous positioning of road-headers is a key link to realize intelligent tunneling, and many scholars have done a great deal of original work in this area. An indoor global position system (IGPS)-based pose measurement method for roadheaders was proposed by [PERSON] et al. [5]. The position coordinates of a receiver can be obtained from a single transmitter station, which realized the autonomous measurement of the body pose in a multi-point time-sharing manner. A remote-controlled system for roadheaders was developed by [PERSON] et al. [6]. The system had built-in active lighting infrared sensors, which could assist in the positioning of a roadheader without visual control by workers. [PERSON] et al. [7] studied the pose measurement method of roadheaders based on machine vision. A laser target was fixed on a roadheader to receive the laser beam. After the imaging features were collected using a monocular camera, the roadheader pose can accurately be solved by solving the pose model and performing error compensation. [PERSON] et al. [8] explored a multi-source fusion measurement method based on ultrasound, inertial navigation, a laser, and geomagnetic information. The method could overcome the shortcoming of excessive accumulated errors in a single inertial navigation measurement and effectively monitor the roadheader pose. [PERSON] et al. [9; 10] developed a set of roadheader pose measurement systems based on an ultra-wideband. A time-of-arrival model was built after the distance between the positioning node of a roadheader and ultra-wideband (UWB) positioning base station was measured. After the positioning algorithm was solved, the roadheader's pose information could be obtained. [PERSON] et al. [11] presented a vision measurement method for the pose of roadheader's cutting head, based on infrared LEDs. A 16-point infrared target was fixed on the roadheader, and then the target image was collected and processed using a monocular camera; the pose of the cutting head could then be estimated. Simulation and experimental results showed that the accuracy of pose measurements could meet the requirements of underground roadway construction. [PERSON] et al. [12] investigated a roadheader pose measurement method based on multi-source information, such as a gyroscope, a total station, and an inertial measurement system, and built a positioning model. Experimental results showed that this method could meet precision requirements. [PERSON] et al. [13] put forward a positioning method for roadheaders based on binocular vision technology. By collecting information with a binocular vision sensor, the coordinate model between a roadheader and the roadway space was established, and then the pose was calculated.