Multi-class classification for semantic labeling of places

Publication Type:
Conference Proceeding
Citation:
11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, 2010, pp. 2307 - 2312
Issue Date:
2010-12-01
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Human robot interaction is an emerging area of research, where human understandable robotic representations can play a major role. Knowledge of semantic labels of places can be used to effectively communicate with people and to develop efficient navigation solutions in complex environments. In this paper, we propose a new approach that enables a robot to learn and classify observations in an indoor environment using a labeled semantic grid map, which is similar to an Occupancy Grid like representation. Classification of the places based on data collected by laser range finder (LRF) is achieved through a machine learning approach, which implements logistic regression as a multi-class classifier. The classifier output is probabilistically fused using independent opinion pool strategy. Appealing experimental results are presented based on a data set gathered in various indoor scenarios. ©2010 IEEE.
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