MonoSLAM：具有全局结构优化的鲁棒单目SLAM
MonoSLAM: Robust Monocular SLAM with Global Structure Optimization
AI总结：
本文提出MonoSLAM，通过从线特征构建消失点并加权融合为全局基元，进行多帧重投影误差优化，以提升低纹理环境下单目视觉SLAM的鲁棒性与跟踪精度。
摘要：
本文提出了一种鲁棒的单目视觉SLAM系统，该系统同时利用点、线和消失点特征来实现精确的相机位姿估计与地图构建。针对在低纹理环境中实现可靠定位这一关键挑战——传统基于点的系统常因视觉特征不足而失效——我们引入了一种利用全局基元（Global Primitives）结构信息的新方法，以提升系统的鲁棒性和精度性能。我们的核心创新在于从线特征中构建消失点，并提出一种加权融合策略，在世界坐标系中构建全局基元。该策略将多个具有非重叠区域的帧关联起来，并构建多帧重投影误差优化，从而显著提升纹理稀缺场景下的跟踪精度。在多个数据集上的评估表明，我们的系统在轨迹精度方面优于现有最优方法，尤其是在具有挑战性的环境中。
英文摘要：
This paper presents a robust monocular visual SLAM system that simultaneously utilizes point, line, and vanishing point features for accurate camera pose estimation and mapping. To address the critical challenge of achieving reliable localization in low-texture environments, where traditional point-based systems often fail due to insufficient visual features, we introduce a novel approach leveraging Global Primitives structural information to improve the system's robustness and accuracy performance. Our key innovation lies in constructing vanishing points from line features and proposing a weighted fusion strategy to build Global Primitives in the world coordinate system. This strategy associates multiple frames with non-overlapping regions and formulates a multi-frame reprojection error optimization, significantly improving tracking accuracy in texture-scarce scenarios. Evaluations on various datasets show that our system outperforms state-of-the-art methods in trajectory precision, particularly in challenging environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
