Activity-Based Semantic Mapping of an Urban Environment

We address the problem of semantic mapping using mobile robots. We focus on the problem of mapping activity as a precursor to automatically classifying, modeling and ultimately understanding the usage of space in a typical urban outdoor environment. We propose and compare two methods for activity mapping - one based on hidden Markov models and the other based on support vector machines. Both approaches estimate high level properties of space based on low level sensor data using supervised learning to associate features to desired classification patterns.