RGB-D SLAM System Based on BA Optimization and KL Divergence

doi:10.12068/j.issn.1005-3026.2020.07.004

Abstract

Abstract: To reduce the impact of noise in depth image acquisition on the accuracy of pose estimation and to solve the serious problem of cumulative error over time， an improved RGB-D SLAM system was designed. Firstly， the re-projection error model was established to nonlinearly optimize the poses and features by minimizing the re-projection error. In addition， a closed-loop detection algorithm was proposed. The dictionary model was established and the frequency-inverse document frequency(TF-IDF) was used to calculate the weight. Kullback-Leibler divergence was used to calculate the similarity， and a relative similarity mechanism was used for the closed-loop detection. The cumulative error was decreased. The algorithm was evaluated using 15 public image sequences. Compared with three popular RGB-D SLAM systems， the maximum increase of accuracy is averagely 119.07% and the minimum increase is 4.24%. Experimental results show that the proposed method has better accuracy than the current popular RGB-D SLAM systems.

Key words: machine vision, simultaneous localization and mapping, BA optimization, KL divergence, closed-loop detection

CLC Number:

TP242.6

XU Yan， AN Wei-feng. RGB-D SLAM System Based on BA Optimization and KL Divergence[J]. Journal of Northeastern University Natural Science, 2020, 41(7): 933-937.

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