# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 1 Introduction
Simulations and experiments  
Figure 1: Research hotspots of intelligent coal mines from 2010 to 2020.  
showed that the accuracy of the calculated positioning parameters was good and the established positioning model was reasonable. [PERSON] et al. [14] proposed a vision measurement method for the pose of a roadheader body based on laser beams. The laser beams were used for the image feature, and a measurement system of roadheader body pose, based on monocular vision, was established. Experimental results showed that this method could effectively solve body pose information of roadheaders. A pose measurement method for a roadheader, combining total station and strapdown inertial navigation, was proposed by [PERSON] et al. [15]. Total station and inertial navigation technology were used to measure the pose of roadheaders, respectively, and the pose was determined using fusion data, which reduced the influence of dust in the mine. The above research covers the main schemes of present pose measurement of roadheaders. Total station has high installation flexibility, but its light path is easily blocked, and its real-time performance is poor and cannot adapt to the dynamic working conditions of a roadheader. An inertial sensor is generally used as an auxiliary measurement method because of its large cumulative error. UWB technology has a strong real-time performance, but its signal is greatly affected by the complex electromagnetic environment of coal mines. IGPS technology has a large measurement range, but the cost of installation and construction is high. Visual measurements based on target imaging are susceptible to the influence of light beam scattering, and the installation of a light source emitter is limited by complicated roadway structures. The composition of the combined measurement system is complex, and the multi-source data are difficult to synchronize accurately in time and space; thus, the data reliability is not high. State perception, trajectory planning, and motion control are the three keys to realize intelligent and robotized roadheaders. As a core technology of autonomous positioning and navigation for mobile devices, simultaneous localization and mapping (SLAM) is widely used in mobile robot navigation, unmanned driving, and in other fields [16; 17; 18]. For instance, the open source library, Real-Time Appearance-Based Mapping (RTAB-MAP), provides a positioning and mapping solution that is independent of time and scale, which can be applied to various setups [19]. According to different sensor types, SLAM technology is mainly divided into laser-based and vision-based [20]. The point cloud obtained by LiDAR is sparse, a created map lacks semantic information, and the linearization error is large. In contrast, vision sensors can obtain far more status information than LiDARs. Furthermore, the RGB-D (red, green, blue, and depth) camera in a vision sensor is based on active ranging technology, and it has the advantages of not being affected by ambient lighting changes and textures compared with monocular and binocular cameras. In summary, SLAM technology is a better solution to realize autonomous positioning and navigation of roadheaders and roadway mapping in a GPS-free roadway environment. At present, there are no reports on the application of visual SLAM technology to coal roadway roadheaders, and research on this technology is still lacking. Therefore, the positioning requirements of roadheaders and the objective conditions, such as tunnel environment and sensor attributes, are comprehensively considered, a method of body pose estimation for roadheaders, based on visual SLAM, is proposed, and multiple schemes of environmental map construction based on laser and visual SLAM are tested. These can provide information for pose correction, navigation of roadheaders, and three-dimensional reconstruction of roadways.