ICRA 2011 Paper Abstract


Paper ThP211.6

Fernández Alcantarilla, Pablo (University of Alcalá), Ni, Kai (Georgia Institute of Technology), Bergasa, Luis Miguel (University of Alcala), Dellaert, Frank (Georgia Institute of Technology)

Visibility Learning in Large-Scale Urban Environment

Scheduled for presentation during the Regular Sessions "Visual Servoing II" (ThP211), Thursday, May 12, 2011, 16:40−16:55, Room 5F

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on August 18, 2019

Keywords Computer Vision for Robotics and Automation, Visual Learning, SLAM


A crucial step in many vision based applications, such as localization and structure from motion, is the data association between a large map of known 3D points and 2D features perceived by a new camera. In this paper, we propose a novel approach to predict the visibilities of known 3D points with respect to a query camera in large-scale environments. In our approach, we model the visibility of each 3D point with respect to a camera pose using a memory-based learning algorithm, in which a distance metric between cameras is learned in an entirely non-parametric way. We show that by fully exploiting the geometric relationships between the 3D map and the camera poses, as well as the related appearance information, the resulting prediction is much more robust and efficient than conventional approaches. We demonstrate the performance of our algorithm on a large urban 3D model in terms of both speed and accuracy.



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