BROWSE
Detail
Conference Paper
Robust Regression to Varying Data Distribution and Its Application to Landmark-based Localization
Cited 20 time in 
Share 
Share
- Authors
- Sung Lok Choi, Jong-Hwan Kim
- Issue Date
- 2008-10
- Citation
- International Conference on Systems, Man and Cybernetics (SMC) 2008, pp.3465-3470
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Data may be wrongly measured or come from other sources. Such data is a big problem in regression, which retrieve parameters from data. Random Sample Consensus (RANSAC) and Maximum Likelihood Estimation Sample Consensus (MLE-SAC) are representative researches, which focused on this problem. However, they do not cope with varying data distribution because they need to tune variables according to given data. This paper proposes user-independent parameter estimator, u-MLESAC, which is based on MLESAC. It estimates variables necessary in probabilistic error model through expectation maximization (EM). It also terminates adaptively using failure rate and error tolerance, which can control trade-off between accuracy and running time. Line fitting experiments showed its high accuracy and robustness in varying data distribution. Its results are compared with other estimators. Its application to landmark-based localization also verified its performance compared with other estimator. © 2008 IEEE.
- KSP Keywords
- Data Distribution, Error model, High accuracy, Parameter estimator, Random sample consensus, Robust regression, Running Time, Trade-off, error tolerance, expectation maximization(EM), failure rate