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Augmented reality (AR) displays become more and more popular recently,because of its high intuitiveness for humans and high-quality head-mounteddisplay have rapidly developed. To achieve such displays with augmentedinformation, highly accurate image registration or ego-positioning arerequired, but little attention have been paid for out-door environments. Thispaper presents a method for ego-positioning in outdoor environments with lowcost monocular cameras. To reduce the computational and memory requirements aswell as the communication overheads, we formulate the model compressionalgorithm as a weighted k-cover problem for better preserving model structures.Specifically for real-world vision-based positioning applications, we considerthe issues with large scene change and propose a model update algorithm totackle these problems. A long- term positioning dataset with more than onemonth, 106 sessions, and 14,275 images is constructed. Based on both local andup-to-date models constructed in our approach, extensive experimental resultsshow that high positioning accuracy (mean ~ 30.9cm, stdev. ~ 15.4cm) can beachieved, which outperforms existing vision-based algorithms.

To Know Where We Are: Vision-Based Positioning in Outdoor Environments