An improved ORB-SLAM3 feature matching algorithm based on GMS
Aiming to solve the problem of low accuracy of ORB feature point matching in ORB-SLAM3,this paper proposes an improved feature point matching strategy.First,considering that feature point extraction and matching are affected by dim scenes and low contrast,this paper enhances the contrast and denoises the datasets of dim scenes.Second,in order to increase the number and speed of feature point matching,motion smoothness constraints are used as the basis for removing incorrect feature point matches,abandoning rotation invariance and scale invariance,and converting the image into a 3×3 grid to accelerate computation.Finally,to improve the accuracy of feature matching,this paper considers the relationship between distance and confidence by calculating the number of matches within the neighborhood of feature points and comparing it with a set threshold to filter out correct matches.Subsequent,camera pose estimation and visual odometry calculations are performed to estimate the camera's movement path.Through experimental analysis,this method is shown to increase the number of correct feature point matches by approximately 72.8％and reduce the matching time by about 9％.After conducting experiments on RGB-D and Euroc datasets,the localization accuracy for dim datasets increased by approximately 21.20％and 63.67％,respectively.Compared to other methods,this approach not only enhances the system's processing speed and robustness but also improves average localization accuracy.
ORB feature pointsfront-end optimizationfeature point extraction and matchingreal-timeSLAM
万方数据
维普
