In the field of Simultaneous Localization and Mapping (SLAM), researchers have always pursued better performance in terms of accuracy and time cost. Traditional algorithms typically rely on fundamental geometric elements in images to establish connections between frames. However, these elements suffer from disadvantages such as uneven distribution and slow extraction. In addition, geometry elements like lines have not been fully utilized in the process of pose estimation. To address these challenges, we propose GFS-VO, a grid-based RGB-D visual odometry algorithm that maximizes the utilization of both point and line features. Our algorithm incorporates fast line extraction and a stable line homogenization scheme to improve feature processing. To fully leverage hidden elements in the scene, we introduce Manhattan Axes (MA) to provide constraints between local map and current frame. Additionally, we have designed an algorithm based on breadth-first search for extracting plane normal vectors. To evaluate the performance of GFS-VO, we conducted extensive experiments. The results demonstrate that our proposed algorithm exhibits significant improvements in both time cost and accuracy compared to existing approaches.

翻译：在同时定位与地图构建（SLAM）领域，研究者始终追求在精度和时间成本方面的更优性能。传统算法通常依赖图像中的基础几何元素建立帧间关联，然而这些元素存在分布不均匀、提取速度慢等缺陷。此外，诸如直线等几何元素在姿态估计过程中尚未得到充分利用。针对这些挑战，我们提出GFS-VO——一种基于网格的RGB-D视觉里程计算法，可最大化利用点特征和线特征。该算法融合了快速线特征提取与稳定的线特征均匀化方案以改进特征处理。为充分挖掘场景中的隐藏元素，我们引入曼哈顿轴（MA）约束局部地图与当前帧的关联，并设计了基于广度优先搜索的平面法向量提取算法。通过大量实验评估GFS-VO性能，结果表明所提算法在时间成本和精度方面均较现有方法有显著提升。