基于支持向量机的空间配准方法

2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF) Pub Date : 1900-01-01 DOI:10.1109/ICEDIF.2015.7280177

Z. Niu, Chaowei Chang, Teng Li

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引用次数: 1

摘要

本文研究了非参数估计的特点和适用性。提出了一种基于支持向量机的空间配准方法。将该方法与基于神经网络的传感器配准方法和基于广义最小二乘估计的多类参数传感器配准方法进行了比较。结果表明，基于支持向量机的空间配准方法是有效的。

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An approach for space registration based on support vector machine

The characteristic and applicability of nonparametric estimation are studied in this paper. A method of space registration based on support vector machine (SVM) is proposed. It is compared with the method of sensor registration based on neural network and the method of generalized least square estimator (GLS) in multi-kind parameters. The results illustrate that the method of space registration based on support vector machine is effective.

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2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF)

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