This letter presents an efficient and accurate simultaneous localization and mapping (SLAM) system in man-made environments. The Manhattan world assumption is imposed, with which the global orientation is obtained. The drift-free rotational motion estimation is derived from the structural regularities using line features. In particular, a two-stage vanishing points (VPs) estimation method is developed, which consists of a short-term tracking module to track the clustered line features and a long-term searching module to generate abundant sets of VPs candidates and retrieve the optimal one. A least square problem is constructed and solved to provide refined VPs with the clusters of structural line features every frame. We make full use of the absolute orientation estimation to benefit the whole SLAM process. In particular, we utilize the absolute orientation estimation to increase the localization accuracy in the front end, and formulate a linear batch camera pose refinement problem with the known rotations to improve the real time performance in the back end. Experiments on both synthesized and real-world scenes reveal results with high-precision in the real time camera pose estimation process and high-speed in pose graph optimization process compared with the existing state-of-the-art methods.

中文翻译：

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曼哈顿世界中具有无漂移旋转估计的视觉 SLAM

这封信提出了一种在人造环境中高效准确的同步定位和地图构建 (SLAM) 系统。曼哈顿世界假设被强加，由此获得全球方向。无漂移旋转运动估计是从使用线特征的结构规律推导出来的。特别地，开发了一种两阶段消失点（VPs）估计方法，它由一个短期跟踪模块来跟踪聚类线特征和一个长期搜索模块来生成丰富的 VPs 候选集并检索最佳一。构建并解决最小二乘问题，以提供具有每帧结构线特征簇的精细 VP。我们充分利用绝对方向估计来使整个 SLAM 过程受益。特别是，我们利用绝对方向估计来提高前端的定位精度，并使用已知旋转制定线性批量相机姿态细化问题，以提高后端的实时性能。与现有的最先进方法相比，在合成场景和真实世界场景上的实验揭示了实时相机姿态估计过程的高精度和姿态图优化过程的高速结果。