Abstract
In this paper, a method using MLESAC(Maximum Likelihood Estimation SAmple Consensus) algorithm is proposed to improve positioning accuracy of the curved section when building 3D map using 2D LRF(Laser Range Finder). RANSAC(RANdom SAmple Consensus) algorithm predicts the solution by iteratively extending a consistent set of data to derive a reasonable result. When the mobile robot suddenly rotates within one frame of measurement of the LRF using a servo motor, positioning accuracy of the curved section in 3D map is degraded. In this case, RANSAC algorithm can be used to improve positioning accuracy of the curved section in 3D map by iteratively classifying the data received from outside of measurement area as outliers and isolating them. RANSAC algorithm defines a loss function as zero if the error is less than noise threshold and as positive constant if the error is greater than noise threshold. Therefore, a limitation exists in reflecting the form of data densification as it approaches a specific model within noise threshold, such as Gaussian distribution. In case of MLESAC algorithm, finding the densest data set helps improving positioning accuracy by showing smaller loss values as the data approach the particular model. The proposed method using MLESAC algorithm is experimentally validated.
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