A rapid optimization method for visual indirect SLAM using a subset of feature points

In this paper, we propose a novel optimization method for indirect Simultaneous Localization And Mapping(SLAM). This method avoids error-intended map points using a random subset of extracted feature points. As a result, this method making the calculation time short at the cost of some accuracy. The published code of ORB-SLAM2 has longer calculation time compared to other systems such as Semi-direct odometry(SVO). This is due to feature extraction time of an indirect method, which makes the indirect methods robust. However, considering CPU performance on edge devices like MAVs, a lightweight system is welcome. ORB-SLAM2 is widely known as a robust and rapid visual Simultaneous-Localization-And-Mapping (vSLAM) method than before. It extracts FAST feature points from a given image and optimizes using all of these points for camera-posing estimation. However, some points disturb accurate estimation causing large reprojection error after optimization. Although these points are eliminated as outliers after optimization, they may be an obstacle for accurate pose estimation. To make this system more robust and more rapid, We suggest an improved method to estimate camera-posing, where we use a subset of extracted points for estimation, avoiding mixing large-reprojection-error points in bundle adjustment. The experimental results on the SLAM benchmark dataset KITTI odometry demonstrated that the proposed method outperformed the original implementation of ORB-SLAM2 from the viewpoint of calculation time.