With the development of computer vision and deep learning,vision-based static SLAM research has been improved,however,most SLAM algorithms suffer from untenable static assumptions and cumulative drift.To address these problems,dy-namic BN-SLAM algorithms were designed to correct the original mask using the geometric information of the depth image,and the correction mask was used to remove moving objects and their effects.The weighted RANSAC method was designed to solve the local camera position.Experimental results on the TUM dataset show that the average RMSE values of ATE,translational RPE and rotational RPE of BN-SLAM are 95.46％,92.45％and 90.88％,respectively.The average S.D.values are 94.88％,94.76％and 92.80％,respectively.The average rate of tracking the trajectory point results is 98.80％.Results of experiments in real environments show that the BN-SLAM is able to eliminate map contamination caused by moving people.
维普
万方数据