Óscar Martínez Mozos.
Supervised Learning of Places from Range Data using AdaBoost.
Master's Thesis. University of Freiburg. December 2004.

Finalist: ICRA Best Student Paper
For the paper of my thesis:
Óscar Martínez Mozos, Cyrill Stachniss, Wolfram Burgard.
Supervised Learning of Places from Range Data Using AdaBoost.
IEEE International Conference on Robotics and Automation (ICRA).
pp. 1742-1747. Barcelona, Spain. April, 2005.


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.

Thesis: [pdf: 1370k] [ps.gz: 1732k (better image quality)]


    title   =   {Supervised Learning of Places from Range Data using AdaBoost},
    author  =   {Oscar Martinez Mozos},
    school  =   {University of Freiburg},
    month   =   {December},
    year    =   {2004},
	url 	=   {http://www.informatik.uni-freiburg.de/~omartine/publications/thesis.pdf}


Online classification with a mobile robot.
See how places are classified and colored as the robot moves.
Colors: room(blue), corridor(red), doorway(yellow).
Video: [avi: 77k].