Supervised learning of places from range data using adaboost

In the past, several researchers focused on building accurate metric or topological maps out of sensor data. The majority of approaches present solutions to simultaneous localization and mapping but only a few works try to acquire semantic information autonomously. In this work we address the problem of classifying places in environments into semantic classes based on range data only. We use a supervised learning algorithm to train a set of classifiers based on the Adaboost algorithm. Using our classification system, a mobile robot is able to distinguish different places like rooms, corridors, doorways, and hallways