Vision based model generation for indoor environments

This paper presents our approach to retrieve a dependable three-dimensional description of a partially known indoor environment. We describe the way the sensor data from a video camera is preprocessed by contour tracing to extract the boundary lines of the objects and how this information is transformed into a three-dimensional environmental model of the world. We introduce a dynamic map that operates in a closed loop with various sensor systems improving their performance by filtering and contributing certain knowledge. The filtering relies on the capability of a mobile robot to gather sensor readings from different positions. An important part of our approach is the interaction between the dynamic map, storing and filtering the incoming information, and a module predicting missing sensor features based on structures and reference objects. This interaction helps to generate a more accurate model containing also poor detectable features, that are impossible to extract from a single sensor view