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Developing precise and low-cost spatial localization algorithms is an essential component for autonomous
navigation systems. Data collection must be of sufficient detail to distinguish unique locations, yet coarse enough to
enable real-time processing. Active proximity sensors such as sonar and rangefinders have been used for interior
localization, but sonar sensors are generally coarse and rangefinders are generally expensive. Passive sensors such as
video cameras are low cost and feature-rich, but suffer from high dimensions and excessive bandwidth. This paper
presents a novel approach to indoor localization using a low cost video camera and spherical mirror. Omnidirectional
captured images undergo normalization and unwarping to a canonical representation more suitable for processing.
Training images along with indoor maps are fed into a semi-supervised linear extension of graph embedding manifold
learning algorithm to learn a low dimensional surface which represents the interior of a building. The manifold surface
descriptor is used as a semantic signature for particle filter localization. Test frames are conditioned, mapped to a low
dimensional surface, and then localized via an adaptive particle filter algorithm. These particles are temporally filtered
for the final localization estimate. The proposed method, termed omnivision-based manifold particle filters, reduces
convergence lag and increases overall efficiency.
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