An adaptive keyframe election-based dynamic simultaneous localization and mapping(SLAM)algorithm under large curvature motion is proposed to address the problems of traditional SLAM algorithms,such as missed keyframe election in large curvature motion and failing to accurately judge the motion state of objects in dynamic scenes.In order to avoid the missing keyframes in the curve movement,a local reverse index window is created and the adaptive threshold is calculated based on the number of feature points,matching points and spatial points of regional changes in the keyframe,the current frame and the reference frame in the window.The missed key frames in the curve motion are added to improve the positioning accuracy of the algorithm.Meanwhile,in order to avoid inaccurate thresholds for keyframe selection caused by dynamic points,a dynamic object judgment strategy based on parallax angle model is designed to estimate the motion state of potential dynamic objects.Tested on public datasets and real scenes,the results show that compared with DynaSLAM algorithm,the average absolute trajectory error of the proposed algorithm is reduced by 20%in the TUM dataset,and the positioning accuracy is improved by 12.1%and 15.3%respectively in indoor and outdoor dynamic scenes with large curvature,which demonstrates a good mapping ability.
关键词
同步定位与地图构建/大曲率运动/关键帧/自适应阈值/动态场景/运动判断
Key words
simultaneous localization and mapping/large curvature motion/keyframe/adaptive thresholds/dynamic scenes/motion judgment
维普
万方数据