Visual SLAM Based on Constant Velocity Model in Dynamic Environment
In recent years,with its rapid development,simultaneous localization and map creation(SLAM)technology has been widely used in autonomous driving,sweeping robots,AGV and other intelligent machines,realizing the autonomous move-ment of intelligent bodies in unknown environments.At present,visual SLAM is mainly based on feature point method to calculate pose.However,in dynamic environment,dynamic feature points will seriously affect the estimation of pose and the construction of map points.And the match of feature points will take up a lot of computing time.Therefore,a visual SLAM algorithm based on con-stant velocity model in dynamic environment is proposed in this paper.This algorithm uses constant velocity model to track feature points,which avoids global matching of feature points in each frame and greatly reduces computing time for matching.It uses the mo-tion consistency detection algorithm and semantic segmentation algorithm to detect dynamic feature points in dynamic environment and mark the region of feature points as dynamic objects,and then all feature points on dynamic objects are deleted.Experimental results show that compared with ORB_SLAM,this algorithm has higher accuracy and real-time performance when there are a large number of moving objects.
国家科技期刊平台
NSTL
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