We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time in standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our backend based on Bundle Adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches to map points that allow for zero-drift localization. The evaluation in 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

ORB-SLAM2是一个用于单目、双目以及RGB-D相机的完整的SLAM系统，包括地图重用、回环闭合和重定位能力，利用纯视觉观测、双目视觉观测和RGB-D相机，在各种环境下都能实时工作。其基于捆绑调整的后端可以在实现度规尺度精确的同时实现高精度的轨迹估计。通过在29个流行的公共数据集上的评估，证明ORB-SLAM2的精度达到了最新的最佳结果，因此我们将其开源以便于SLAM社区以及其他领域的研究人员使用。

ORB-SLAM2: 一个适用于单目相机、立体相机与RGB-D相机的开源SLAM系统