Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank- 1 matrix factorization technique which is more robust to the errors in map initialization. Second, we adopt a re- cent global SfM method for the pose-graph optimization, which leads to a multi-stage linear formulation and enables L1 optimization for better robustness to false loops. The combination of these two approaches generates more robust reconstruction and is significantly faster (4X) than recent state-of-the-art SLAM systems. We also present a new dataset recorded with ground truth camera motion in a Vicon motion capture room, and compare our method to prior systems on it and established benchmark datasets.

该论文提出了一种结合全局SfM方法的新型单目SLAM方法，使用新颖的秩1矩阵分解技术解决了视觉里程计问题，并采用多阶段线性公式和L1优化进行姿势图优化，生成了更为稳健的重建结果，比最新的SLAM系统快4倍，并在Vicon运动捕捉房间记录的新数据集上与之前的系统和已有基准数据集进行了比较。

GSLAM: 全局运动恢复下单目视觉SLAM的初始化鲁棒性