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Article overview
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To Know Where We Are: Vision-Based Positioning in Outdoor Environments | | Kuan-Wen Chen
; Chun-Hsin Wang
; Xiao Wei
; Qiao Liang
; Ming-Hsuan Yang
; Chu-Song Chen
; Yi-Ping Hung
; | | Date: |
19 Jun 2015 | | Abstract: | Augmented reality (AR) displays become more and more popular recently,
because of its high intuitiveness for humans and high-quality head-mounted
display have rapidly developed. To achieve such displays with augmented
information, highly accurate image registration or ego-positioning are
required, but little attention have been paid for out-door environments. This
paper presents a method for ego-positioning in outdoor environments with low
cost monocular cameras. To reduce the computational and memory requirements as
well as the communication overheads, we formulate the model compression
algorithm as a weighted k-cover problem for better preserving model structures.
Specifically for real-world vision-based positioning applications, we consider
the issues with large scene change and propose a model update algorithm to
tackle these problems. A long- term positioning dataset with more than one
month, 106 sessions, and 14,275 images is constructed. Based on both local and
up-to-date models constructed in our approach, extensive experimental results
show that high positioning accuracy (mean ~ 30.9cm, stdev. ~ 15.4cm) can be
achieved, which outperforms existing vision-based algorithms. | | Source: | arXiv, 1506.5870 | | Services: | Forum | Review | PDF | Favorites | |
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