Mobile robot simultaneous localization and mapping using low cost vision sensors

In this work, an information-based iterative algorithm is proposed to plan a mobile robot's visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2-D panoramic image of the environment from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. Experimental results show the effectiveness of this algorithm.