## 1 Introduction  
Solar energy, as one of the main new energy sources, has become the backbone of the new energy industry [1]. Typically, large-scale photovoltaic power plants are built in geographically complex areas such as valleys and deserts, and utilizing mobile robots to carry out tasks such as cleaning and inspection in the photovoltaic power plants has obvious advantages [2; 3; 4]. The perception of their own status and the external environment by mobile robots is a prerequisite for their autonomous operation in the non-structured environment of photovoltaic power plants [5; 6]. In applications such as autonomous driving, robot positioning, and map building, ORB-SLAM2 [7], as a visual SLAM (simultaneous localization and mapping) method based on image features, has become quite mature in scenes with rich textures and complex structures [8]. However, in special environments like photovoltaic power plants, there are unstructured factors: high feature similarity, weak scene textures, and simple structures. These unstructured factors impose strong interference on the ORB feature matching algorithm in the ORB-SLAM2 algorithm, leading to a decrease in tracking accuracy. The images of the photovoltaic power plant scene are mainly composed of photovoltaic modules. As shown in Figure 1, the image of a photovoltaic module can be seen as a combination of multiple small images with similar texture and structural features. The appearance, shape, and color of photovoltaic modules are similar, lacking unique markings or sufficient distinguishing characteristics. The similar features lead to poor differentiation in feature descriptors, which results in a large number of mismatches during the feature matching stage, subsequently causing incorrect pose estimation. Inaccurate pose estimation can affect the tracking accuracy and mapping effectiveness of the SLAM system and may even result in tracking failures midway through. Figure 1 illustrates that the textures and structures of photovoltaic panels are extremely simple, with the extracted feature points concentrated at the connections of the photovoltaic panels. The limited number of feature points reduces the tracking stability and backend optimization capability of the SLAM system, leading to diminished system robustness. According to the analysis of a large number of feature matching experimental results, it was found that there is an inconsistency in the mismatches between the features in the images of the photovoltaic power plant scene, meaning they do not have the same constraints, whereas correct matches generally have the same constraints. ORB-SLAM2 uses the ORB [9] algorithm for image feature extraction and matching. In this context, this paper focuses on recent research on improving the ORB algorithm. Reference [10] proposed an improved ORB algorithm based on dynamic thresholds and enhanced quadtree methods to enhance the uniformity of feature point extraction. Reference [11] introduced an improved ORB algorithm combining adaptive histogram equalization techniques to improve feature point quality and matching efficiency under low or overexposed conditions. Reference [12] combined an improved ORB algorithm with the Lucas-Kanade algorithm to propose a new image feature matching algorithm, which enhances feature point uniformity and matching accuracy. Reference [13] proposed an improved ORB algorithm based on adaptive thresholds and local grayscale differences,  
Figure 1: Photovoltaic power plant scene images. improving the effectiveness of feature description. References [14; 15] segmented and organized high-dimensional feature data by establishing kd trees and k-means trees, enabling faster nearest neighbor searches and accelerating feature matching speed. Reference [16] incorporated grid motion statistics into feature matching to eliminate incorrect matches, effectively improving feature matching accuracy.