## 1 Introduction
KinectFusion employs the TSDF (Truncated Signed Distance Function) model for real-time integration of RGB-D pictures, facilitating dense surface reconstruction of the environment [11]. As the span of the reconstruction scene develops, the storage space needs of the TSDF model increase substantially. Researchers have suggested enhanced algorithms to tackle this issue. Kintinuous [12] employs a cyclical storage-space-use strategy to improve the reconstruction scene range, whereas ElasticFusion [13] utilizes a Surfel element model for dense map reconstruction. While these methods excel in small-scale scenarios, they encounter limitations in storage and computational resources and exhibit inadequate adaptability in large-scale, dynamic contexts. Another approach, BundleFusion, utilizes conventional Structure from Motion (SFM) technology to align each frame with all previous frames. This approach effectively reconstructs small-scale scenes but necessitates two high-performance GPUs to attain real-time processing speed [14]. [PERSON] et al. [15] introduced a dense mapping framework to enhance sparse visual SLAM systems, attaining globally consistent, dense three-dimensional reconstruction with TSDF fusion technology, utilizing camera poses, keyframes, and sparse points produced by the SLAM system as inputs. These studies offer efficient technical solutions for dense mapping; however, selecting suitable approaches is complex, as many map types are helpful for distinct application circumstances. Conversely, current research emphasizes enhancing feature-matching techniques to bolster the resilience of SLAM systems. Researchers have implemented sparse optical flow, Convolutional Neural Networks (CNNs), and line characteristics to enhance the matching stability [16; 17]. Despite the potential of these strategies to improve matching efficacy, they encounter considerable difficulties in low-texture settings and frequently result in prolonged matching durations [18]. To boost the matching precision and efficiency, the literature [19; 20] presents solutions that integrate point and line segment features to improve robustness in low-texture situations. The literature [21; 22] presents enhanced matching accuracy by superior matching algorithms and outlier rejection techniques; nevertheless, these approaches generally necessitate extended calculation durations. Consequently, enhancing the matching speed while maintaining the matching accuracy is a significant research focus in contemporary SLAM technology. In recent years, many studies have focused on enhancing SLAM system performance through multi-feature fusion and deep learning techniques. For instance, EPLF-VINS [23] improves the line feature quality through short-line fusion and adaptive threshold extraction, proposing a line optical flow tracking method based on gray-level invariance and colinear constraints, significantly accelerating the line feature matching. The UPLC-SLAM [24] introduces an RGB-D visual SLAM method based on point, line, and plane feature fusion, designing a mutual association scheme for these features to unify and jointly optimize matching and association errors, thereby improving the accuracy and consistency of the camera pose estimation and map building. Additionally, a semantic SLAM-based dense mapping approach [25] reduces the impact of moving objects on pose estimation through multi-view projection and optimizes feature points via depth local contrast, generating multiple dense 3D maps for advanced robot navigation. In the agricultural field, a real-time RGB-D SLAM method based on point-line feature fusion [26] constructs 3D scene maps by optimizing the joint poses of point-line features and designs a joint point cloud filtering method based on keyframe optimization, achieving high-quality dense map reconstruction.