剛剛獲悉ORBSLAM3已經發表了論文並且將要開源了，找來論文看了看。
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摘要：
Abstract—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-aPosteriori (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 previousinformation.Thisallowstoincludeinbundleadjustment 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, ORBSLAM3isasrobustasthebestsystemsavailableintheliterature, and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.6cm on the EuRoC drone and 9mm 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.
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orbslam3是第一個能夠執行視覺、視覺慣性和多地圖重擊的系統，這次它的新增元素有下面這幾個：
1.加入了魚眼攝像頭。
2.加入了imu
3.加入了多地圖系統，在ORBslam2中如果圖像跟丟，那么必須回到原來的地方進行重定位，才能繼續跟蹤，而在新的ORBslam3中，如果跟丟，就會新開一個地圖，繼續跟蹤，當回到以前走過的地方，他會合並兩個地圖。還有在所有算法階段都可以重用以前的信息。
精度：EuRoC ：3.6cm ； TUM-VI dataset ： 9mm
框架長這樣
對比效果：
demo參考：