Zhang, Ang;
Min, Zhe;
Zhang, Zhengyan;
Yang, Xing;
Meng, Max Q-H;
(2022)
Anisotropic Generalized Bayesian Coherent Point Drift for Point Set Registration.
IEEE Transactions on Automation Science and Engineering
10.1109/TASE.2022.3159553.
(In press).
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Abstract
Registration is highly demanded in many real-world scenarios such as robotics and automation. Registration is challenging partly due to the fact that the acquired data is usually noisy and has many outliers. In addition, in many practical applications, one point set (PS) usually only covers a partial region of the other PS. Thus, most existing registration algorithms cannot guarantee theoretical convergence. This article presents a novel, robust, and accurate three-dimensional (3D) rigid point set registration (PSR) method, which is achieved by generalizing the state-of-the-art (SOTA) Bayesian coherent point drift (BCPD) theory to the scenario that high-dimensional point sets (PSs) are aligned and the anisotropic positional noise is considered. The high-dimensional point sets typically consist of the positional vectors and normal vectors. On one hand, with the normal vectors, the proposed method is more robust to noise and outliers, and the point correspondences can be found more accurately. On the other hand, incorporating the registration into the BCPD framework will guarantee the algorithm's theoretical convergence. Our contributions in this article are three folds. First, the problem of rigidly aligning two general PSs with normal vectors is incorporated into a variational Bayesian inference framework, which is solved by generalizing the BCPD approach while the anisotropic positional noise is considered. Second, the updated parameters during the algorithm's iterations are given in closed-form or with iterative solutions. Third, extensive experiments have been done to validate the proposed approach and its significant improvements over the BCPD.
| Type: | Article |
|---|---|
| Title: | Anisotropic Generalized Bayesian Coherent Point Drift for Point Set Registration |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TASE.2022.3159553 |
| Publisher version: | http://doi.org/10.1109/TASE.2022.3159553 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Automation & Control Systems, Bayes methods, Hidden Markov models, Convergence, Probabilistic logic, Inference algorithms, Covariance matrices, Three-dimensional displays, Rigid point set registration, correspondence estimation, anisotropic positional error, variational Bayesian inference |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150628 |
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