  While the satellite-based Global Positioning System (GPS) is adequate for
some outdoor applications, many other applications are held back by its
multi-meter positioning errors and poor indoor coverage. In this paper, we
study the feasibility of real-time video-based localization on
resource-constrained platforms. Before commencing a localization task, a
video-based localization system downloads an offline model of a restricted
target environment, such as a set of city streets, or an indoor shopping mall.
The system is then able to localize the user within the model, using only video
as input.
  To enable such a system to run on resource-constrained embedded systems or
smartphones, we (a) propose techniques for efficiently building a 3D model of a
surveyed path, through frame selection and efficient feature matching, (b)
substantially reduce model size by multiple compression techniques, without
sacrificing localization accuracy, (c) propose efficient and concurrent
techniques for feature extraction and matching to enable online localization,
(d) propose a method with interleaved feature matching and optical flow based
tracking to reduce the feature extraction and matching time in online
localization.
  Based on an extensive set of both indoor and outdoor videos, manually
annotated with location ground truth, we demonstrate that sub-meter accuracy,
at real-time rates, is achievable on smart-phone type platforms, despite
challenging video conditions.
