Robust regression to varying data distribution and its application to landmark-based localization
Sunglok Choi, Jong-Hwan Kim · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 2008
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.