A fast learning neural network for oriented visual place map based robot navigation

Research done in two aspects of robot localization and mapping are presented. An online fast learning FLANN, capable of learning location specific spatio-temporally stable visual features is developed. A technique for building oriented place maps using vestibular sensory information that can store multiple pose information of objects is investigated. Unlike most localization and mapping techniques ours does not require any depth estimation and can also handle dynamically changing environments. The system is tested in indoor environments, ranging from very simple to extremely cluttered ones. Preliminary research results show good generalization and learning capabilities of the network and improved localization using multiple oriented place maps.