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Robust 3D Visual Localization Based on RTABmaps

Robust 3D Visual Localization Based on RTABmaps

Alberto Martín Florido, Francisco Rivas Montero, Jose María Cañas Plaza
Copyright: © 2018 |Pages: 17
ISBN13: 9781522556282|ISBN10: 1522556281|EISBN13: 9781522556299
DOI: 10.4018/978-1-5225-5628-2.ch001
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MLA

Florido, Alberto Martín, et al. "Robust 3D Visual Localization Based on RTABmaps." Advancements in Computer Vision and Image Processing, edited by Jose Garcia-Rodriguez, IGI Global, 2018, pp. 1-17. https://doi.org/10.4018/978-1-5225-5628-2.ch001

APA

Florido, A. M., Montero, F. R., & Plaza, J. M. (2018). Robust 3D Visual Localization Based on RTABmaps. In J. Garcia-Rodriguez (Ed.), Advancements in Computer Vision and Image Processing (pp. 1-17). IGI Global. https://doi.org/10.4018/978-1-5225-5628-2.ch001

Chicago

Florido, Alberto Martín, Francisco Rivas Montero, and Jose María Cañas Plaza. "Robust 3D Visual Localization Based on RTABmaps." In Advancements in Computer Vision and Image Processing, edited by Jose Garcia-Rodriguez, 1-17. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5628-2.ch001

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Abstract

Visual localization is a key capability in robotics and in augmented reality applications. It estimates the 3D position of a camera on real time just analyzing the image stream. This chapter presents a robust map-based 3D visual localization system. It relies on maps of the scenarios built with the known tool RTABmap. It consists of three steps on continuous loop: feature points computation on the input frame, matching with feature points on the map keyframes (using kNN and outlier rejection), and 3D final estimation using PnP geometry and optimization. The system has been experimentally validated in several scenarios. In addition, an empirical study of the effect of three matching outlier rejection mechanisms (radio test, fundamental matrix, and homography matrix) on the quality of estimated 3D localization has been performed. The outlier rejection mechanisms, combined themselves or alone, reduce the number of matched feature points but increase their quality, and so, the accuracy of the 3D estimation. The combination of ratio test and homography matrix provides the best results.

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