An Optimized POSIT Algorithm Based on Mean Convergence
Proportional Orthogonal Projection Iteration (POSIT) is the mainstream iterative algorithm in camera pose estimation. The traditional POSIT algorithm has many iterations, too long operating time, and low algorithm robustness. In response to these problems, it proposes a POSIT optimization algorithm...
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| Vydané v: | 2021 International Conference on Communications, Information System and Computer Engineering (CISCE) s. 636 - 640 |
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14.05.2021
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| Abstract | Proportional Orthogonal Projection Iteration (POSIT) is the mainstream iterative algorithm in camera pose estimation. The traditional POSIT algorithm has many iterations, too long operating time, and low algorithm robustness. In response to these problems, it proposes a POSIT optimization algorithm based on mean convergence in this paper. The algorithm is based on the traditional POSIT, which the input of each iteration is not the last output, but is determined by the weighted sum of all the previous outputs. The weights are determined by the differences between the outputs and the mean. In order to reduce the number of iterations of the algorithm and the time complexity, the algorithm pre-stores some of the key-value pairs of input and output in the actual scene into the hash table, and uses Bloom filters to save memory space. In theory, the POSIT algorithm can be used down to O(1). Finally, the algorithm is verified by experiments with other existing algorithms in this paper. The results show that the optimized POSIT has significantly improved accuracy and robustness, and its time complexity is lower than that of the traditional POSIT. |
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| AbstractList | Proportional Orthogonal Projection Iteration (POSIT) is the mainstream iterative algorithm in camera pose estimation. The traditional POSIT algorithm has many iterations, too long operating time, and low algorithm robustness. In response to these problems, it proposes a POSIT optimization algorithm based on mean convergence in this paper. The algorithm is based on the traditional POSIT, which the input of each iteration is not the last output, but is determined by the weighted sum of all the previous outputs. The weights are determined by the differences between the outputs and the mean. In order to reduce the number of iterations of the algorithm and the time complexity, the algorithm pre-stores some of the key-value pairs of input and output in the actual scene into the hash table, and uses Bloom filters to save memory space. In theory, the POSIT algorithm can be used down to O(1). Finally, the algorithm is verified by experiments with other existing algorithms in this paper. The results show that the optimized POSIT has significantly improved accuracy and robustness, and its time complexity is lower than that of the traditional POSIT. |
| Author | Ni, Xibing Tian, Hui Zhou, Chunyue |
| Author_xml | – sequence: 1 givenname: Xibing surname: Ni fullname: Ni, Xibing email: 19125041@bjtu.edu.cn organization: Beijing Jiaotong University, School of Electronic Engineering,Beijing,China – sequence: 2 givenname: Chunyue surname: Zhou fullname: Zhou, Chunyue email: chyzhou@bjtu.edu.cn organization: Beijing Jiaotong University, School of Electronic Engineering,Beijing,China – sequence: 3 givenname: Hui surname: Tian fullname: Tian, Hui email: huitian@griffith.edu.au organization: Griffith University School of Information and Communication Technology,Australia |
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| Snippet | Proportional Orthogonal Projection Iteration (POSIT) is the mainstream iterative algorithm in camera pose estimation. The traditional POSIT algorithm has many... |
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| SubjectTerms | Filtering algorithms Filtering theory machine pose estimation mean convergence Pose estimation POSIT Robustness Software Software algorithms Time complexity |
| Title | An Optimized POSIT Algorithm Based on Mean Convergence |
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