## 1 Introduction  
As intelligent mining technologies are promoted and implemented, conventional mining sites progressively evolve toward automation and uncrewed operations. Using intelligent machinery, low-carbon methodologies, and automated operations has markedly enhanced mining productivity and safety [1]. Open-pit mines present significant challenges to current robotic localization methods. These challenges include intricate topography, areas with minimal texture, and constantly changing lighting conditions. Conventional sparse point cloud mapping techniques, particularly in intricate large-scale mining regions, are hindered by their limited informational capacity, rendering them inadequate at adapting to dynamic mining environments and leading to their failure to deliver detailed maps for navigation and decision making. As a result, dense mapping technology has emerged as a vital developmental trajectory in the domain of SLAM (Simultaneous Localizationand Mapping) in recent years. Dense mapping [2] produces more intricate and comprehensive environmental models by leveraging extensive data acquired from sensors, hence improving map precision and detail representation capabilities. Mining mapping robots are sophisticated systems that seamlessly combine environmental sensing, target recognition, navigation positioning, and motion control technologies, with accurate localization and dense mapping as significant problems. Commonly employed positioning techniques encompass Global Navigation Satellite Systems (GNSS), Light Detection and Ranging (LIDAR), and optical positioning. GNSS is predominantly utilized outdoors, with centimeter-level precision in unobstructed conditions [3]. In open-pit mines, elevated, uneven mining slopes, machinery, and structures can impede or reflect GNSS signals, compromising location accuracy [4]. LIDAR placement provides high precision and robust anti-interference features, yet it is susceptible to point cloud distortion and mismatching during movement and is comparatively costly. Visual positioning with monocular, stereo, or RGB-D cameras offers comprehensive positional data. At the same time, its affordability, energy efficiency, and compactness render it exceptionally appropriate for extensive operational environments, particularly in mobile robot navigation [5]. Vision-based SLAM systems such as the ORB-SLAM series [6; 7] have achieved considerable advancements in feature point extraction and matching. The ORB-SLAM2, a sophisticated visual SLAM system, accommodates monocular, stereo, and RGB-D cameras, facilitating effective localization and mapping. ORB-SLAM3 enhances localization precision and resilience using feature-based, tightly coupled inertial visual odometry, improving dynamic performance [8]. Nonetheless, ORB-SLAM3 continues to employ brute-force descriptor distance searching to acquire multiple matching point pairs during tracking, supplemented by ratio tests to eliminate low-confidence matches or by projecting previously tracked map points onto the current frame to generate rotation histograms for assessing rotational consistency. The two methods of feature point matching are susceptible to mismatches in scenarios with significant camera transformation angles. Moreover, the ORB-SLAM3 predominantly produces sparse maps solely via feature points, lacking comprehensive environmental and structural information, which complicates the inference of spatial structures between feature points and precludes advanced applications, such as navigation, obstacle avoidance, and reconstruction [9]. The intricate operational context of open-pit mines renders ORB feature-based sparse mapping techniques susceptible to failure, compromising positioning precision and navigation efficacy. Consequently, detailed mapping technology is essential for addressing these issues. RGB-D cameras have provided new developmental options for dense SLAM technology. Microsoft's Kinect [10] offered an innovative approach for real-time dense reconstruction systems with RGB-D cameras.