ORB-SLAM3摘要翻译

ref
Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, Juan D. Tardós
https://arxiv.org/abs/2007.11898

本文介绍了ORB-SLAM3，这一个首创能够基于视觉/视觉惯导/多地图的SLAM系统，可以使用单目，双目，RGBD相机（针孔/鱼眼相机模型）。

第一个创新点，它完全依赖最大后验MAP估计，进行基于特征点的视觉惯导紧融合，在IMU初始化阶段也是如此。
此系统可以实时地鲁棒地在或大或小，室内室外环境中运行，比之前的系统精度提高2-5倍。

第二个主要创新是多地图系统，基于提高了召回率的场景重识别方法，使得ORB-SLAM3可以在不佳的环境中长时间运行。当发生丢失时，它创建新地图，当重新到访建立过地图的地点，新地图回无缝地合并到老地图中。对比只用前几秒信息的视觉里程计系统，ORB-SLAM3是第一个能够在所有阶段重新利用之前所有信息的系统。基于co-visible关键帧的Bundle adjustment方法，使得即使两个信息其相隔的时间很远，或者来自于之前的建图阶段。也能够有较高的是差来提高准确度。

我们的实验表明，在利用所有传感器时，ORB-SLAM3和最好的系统鲁棒性相同，并且显著地更加精确。
值得注意的是，我们的双目惯导SLAM系统，在EuRoc飞行器数据集上达到了3.6cm的准确度，在TUM-VI手持快速运动的数据集（代表AR/VR场景设置）上达到9mm的精度。为了进益SLAM发展，我们公开了ORB-SLAM3代码
（翻译仅供参考by jiayao）

原文：
This paper presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models. The first main novelty is a feature-based tightly-integrated visual-inertial SLAM system that fully relies on Maximum-a-Posteriori (MAP) estimation, even during the IMU initialization phase. The result is a system that operates robustly in real-time, in small and large, indoor and outdoor environments, and is 2 to 5 times more accurate than previous approaches. The second main novelty is a multiple map system that relies on a new place recognition method with improved recall. Thanks to it, ORB-SLAM3 is able to survive to long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting mapped areas. Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information. This allows to include in bundle adjustment co-visible keyframes, that provide high parallax observations boosting accuracy, even if they are widely separated in time or if they come from a previous mapping session. Our experiments show that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.6 cm on the EuRoC drone and 9 mm under quick hand-held motions in the room of TUM-VI dataset, a setting representative of AR/VR scenarios. For the benefit of the community we make public the source code.