基于改进关键帧选择的ORB-SLAM3算法

针对视觉同时定位与地图构建（VSLAM）领域关键帧选择方法多为启发式阈值的方式，容易存在定位精度不高等问题，提出了基于改进关键帧选择的ORB-SLAM3算法。该算法从考虑关键帧质量出发，采用如下三个标准代替ORB-SLAM3中启发式阈值方式，首先是基于几何约束的自适应关键帧阈值，确保特征点数量充足；其次是基于图像质心原理的分布标准，确保特征点分布均匀；最后是惯性测量单元（IMU）加速度状态观测。在EuRoC数据集下的实验结果表明，所改进的算法在不使用启发式阈值的情况下，单目惯性模式下将ORB-SLAM3定位精度提高了9%，双目惯性模式下定位精度提高了6%，表现出更好的鲁棒性和构图能力。

Improved key frames selection algorithm based on ORB-SLAM3

In the field of visual simultaneous localization and mapping(VSLAM), the key-frame selection method is mostly heuristic threshold method. This method is prone to the problem of low positioning accuracy. This paper proposed the ORB-SLAM3 algorithm based on improved key-frame selection in response to this problem. Starting from the consideration of key-frame quality, the algorithm adopted the following three standards instead of the heuristic threshold method in ORB-SLAM3. The first was an adaptive key-frame threshold based on geometric constraints to ensure a sufficient number of feature points. The second was the distribution standard based on the principle of image centroid to ensure that the feature points were evenly distributed. The last was the inertial measurement unit(IMU) acceleration state observation. Experimental results on the EuRoc dataset show that the improved algorithm improves the positioning accuracy of ORB-SLAM3 by 9% in monocular inertial and 6% in stereo inertial without using the heuristic threshold. This algorithm shows better robustness and composition ability than ORB-SLAM3.