Abstract
Room categorization, that is, recognizing the functionality of a never before seen room, is a crucial capability for a household mobile robot. We present a new approach for room categorization that is based on two-dimensional laser range data. The method is based on a novel spatial model consisting of mid-level parts that are built on top of a low-level part-based representation. The approach is then fused with a vision-based method for room categorization, which is also based on a spatial model consisting of mid-level visual parts. In addition, we propose a new discriminative dictionary learning technique that is applied for part-dictionary selection in both laser-based and vision-based modalities. Finally, we present a comparative analysis between laser-based, vision-based, and laser-vision-fusion-based approaches in a uniform part-based framework, which is evaluated on a large dataset with several categories of rooms from domestic environments.
Original language | English |
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Pages (from-to) | 379-402 |
Number of pages | 24 |
Journal | The International Journal of Robotics Research |
Volume | 36 |
Issue number | 4 |
Early online date | 26 Apr 2017 |
DOIs | |
Publication status | Published - Apr 2017 |
Keywords
- room categorization
- part-based models
- discriminative dictionary learning
- laser-vision fusion