Affine-Invariant Geometric Constraints-Based High Accuracy Simultaneous Localization and Mapping. (9th February 2017)

• Record Type: Journal Article
• Authors: Hua, Gangchen; Tan, Xu
• Other Names: Gao Hao Academic Editor.
• Is Part Of: Journal of sensors. Volume 2017(2017)
• Publication Date: 2017-02-09
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2017/1969351
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : In this study we describe a new appearance-based loop-closure detection method for online incremental simultaneous localization and mapping (SLAM) using affine-invariant-based geometric constraints. Unlike other pure bag-of-words-based approaches, our proposed method uses geometric constraints as a supplement to improve accuracy. By establishing an affine-invariant hypothesis, the proposed method excludes incorrect visual words and calculates the dispersion of correctly matched visual words to improve the accuracy of the likelihood calculation. In addition, camera's intrinsic parameters and distortion coefficients are adequate for this method. 3D measuring is not necessary. We use the mechanism of Long-Term Memory and Working Memory (WM) to manage the memory. Only a limited size of the WM is used for loop-closure detection; therefore the proposed method is suitable for large-scale real-time SLAM. We tested our method using the CityCenter and Lip6Indoor datasets. Our proposed method results can effectively correct the typical false-positive localization of previous methods, thus gaining better recall ratios and better precision.