Model-based light stripe detection for indoor navigation

Light stripe projection (LSP) is one of the most robust 3D recognition methods, and the general method of light stripe feature (LSF) detection is Laplacian of Gaussian (LOG) filtering. If distances to objects are various, as in the case of indoor navigation, LSF width becomes various according to distance. As the window size of spatial filtering influences the performance significantly, various LSF widths disturb LOG-based LSF detection with constant base length, that is constant window size. The irradiance maps of LSFs were reconstructed by high dynamic range imaging (HDRi) while changing the distance. By analyzing the irradiance maps, LSF irradiance map was modeled as a 2D Gaussian function whose parameters were approximated as functions of distance. After deriving LSF width function of distance, it was transformed into LSF width function of pixel coordinates by one-to-one relation between distance and y coordinates in LSP. The LSF width function can provide proper base length to LOG filtering. A self-calibration procedure is proposed which can estimate the parameters of LSF width function with only several images captured at new environment. Experimental results show that proposed model-based LSF detection overcomes normal LOG-based LSF detection.