ORB_SLAM3初试

源码地址：
orb_salm3_github
论文地址:
orb_slam3_paper

ORB_SLAM3提供了单目，双目，rgbd,以及各自结合IMU的数据分析实现。

1. demo运行效果展示：
   
   在不同楼层的定位结果

在盖住相机后，定位未跑飞

楼梯下滑过程中，定位主要依靠IMU。

2. 创新点：

论文中提到了最新开源的orb_slam3提到的两点创新点：
（1）全程使用紧耦合的基于特征点的最大后验估计，包括在IMU的初始化阶段。使得系统的精度比旧方法提高的2-5倍，鲁棒性在室内外环境也得到提高，同时也保证实时性。
（2）多地图复用。使用了新的回环方法和回环触发条件。能利用之前所有关键帧信息。能有效提高长期定位精度。
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

3. 与现有技术对比分析：
   论文中列出了现阶段最优秀的一些算法，如基于特征点图优化的VINS-FUSION，基于滤波的MSCKF，基于直接法的SDM,DSO能建立稠密地图，半直接法的SVO等等。其中orb_slam3提供的选择接口最多，6种组合均有涉及，同时在EuRoC数据集中，精度达到最高。
   

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