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Conference Papers Year : 2021

Robust Pose Estimation Based on Maximum Correntropy Criterion

Qian Zhang
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Badong Chen
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Pose estimation is a key problem in computer vision, which is commonly used in augmented reality, robotics and navigation. The classical orthogonal iterative (OI) pose estimation algorithm builds its cost function based on the minimum mean square error (MMSE), which performs well when data disturbed by Gaussian noise. But even a small number of outliers will make OI unstable. In order to deal with outliers problem, in this paper, we establish a new cost function based on maximum correntropy criterion (MCC) and propose an accurate and robust correntropy-based OI (COI) pose estimation method. The proposed COI utilizes the advantages of correntropy to eliminate the bad effects of outliers, which can enhance the performance in the pose estimation problems with noise and outliers. In addition, our method does not need an extra outliers detection stage. Finally, we verify the effectiveness of our method in synthetic and real data experiments. Experimental results show that the COI can effectively combat outliers and achieve better performance than state-of-the-art algorithms, especially in the environments with a small number of outliers.
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Dates and versions

hal-03287697 , version 1 (15-07-2021)





Qian Zhang, Badong Chen. Robust Pose Estimation Based on Maximum Correntropy Criterion. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.555-566, ⟨10.1007/978-3-030-79150-6_44⟩. ⟨hal-03287697⟩
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