Computer Vision based Indoor Navigation Utilizing Information from Planar Surfaces

Abstract

Traditional wireless signalling based outdoor navigation techniques generally result in unsatisfactory performance for indoor environments due to low signal strength and multipath distortions. Computer vision (CV) sensors, due to their low cost and high performance, have gained enormous interest in indoor navigation over the past years. CV based 6DOF trajectory estimation is understood to be a computationally intensive ill-posed problem. Drastic simplification and enhanced robustness are possible in scenarios where camera observed features are constrained to a plane, such as a floor surface. Furthermore, if the features have geometric patterns, such as a regularly tiled surface, significantly more powerful constraints can be implemented. Exploration of such constraints is the aim of this thesis. Experimental results show that centimeter level accuracy in trajectory estimation can be achieved for arbitrary camera motion spanning several meters. As shown in this thesis, this accuracy is a result of constraints due to planar features observed.