论文标题
结构 - 峰：低饮用的室内环境中的单眼大满贯
Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments
论文作者
论文摘要
在本文中，提出了一种针对室内场景的低饮食单眼大满贯方法，由于缺乏纹理表面，因此单眼猛击经常失败。我们的方法将跟踪过程的旋转和翻译估计解除，以减少室内环境中的长期漂移。为了充分利用场景中可用的几何信息，从每个输入RGB图像实时的卷积神经网络可以预测表面正常。首先，使用球形平均移位聚类基于线条和表面正态估算无漂移的旋转，利用了较弱的曼哈顿世界假设。然后，从点和行特征计算翻译。最后，估计的姿势通过地图到框架优化策略进行了完善。所提出的方法在常见的SLAM基准（例如ICL-NUIM和TUM RGB-d）上优于最新方法。
In this paper a low-drift monocular SLAM method is proposed targeting indoor scenarios, where monocular SLAM often fails due to the lack of textured surfaces. Our approach decouples rotation and translation estimation of the tracking process to reduce the long-term drift in indoor environments. In order to take full advantage of the available geometric information in the scene, surface normals are predicted by a convolutional neural network from each input RGB image in real-time. First, a drift-free rotation is estimated based on lines and surface normals using spherical mean-shift clustering, leveraging the weak Manhattan World assumption. Then translation is computed from point and line features. Finally, the estimated poses are refined with a map-to-frame optimization strategy. The proposed method outperforms the state of the art on common SLAM benchmarks such as ICL-NUIM and TUM RGB-D.
