A tale of two object recognition methods for mobile robots

Authors:
Arnau Ramisa;Shrihari Vasudevan;Davide Scaramuzza;Ramón López de Mántaras;Roland Siegwart
Affiliations:
Artificial Intelligence Research Institute, CSIC, Bellaterra, Spain;Autonomous Systems Lab, ETH Zurich, Switzerland;Autonomous Systems Lab, ETH Zurich, Switzerland;Artificial Intelligence Research Institute, UAB, Bellaterra, Spain;Autonomous Systems Lab, ETH Zurich, Switzerland
Venue:
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Year:
2008

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Abstract

Object recognition is a key feature for building robots capable of moving and performing tasks in human environments. However, current object recognition research largely ignores the problems that the mobile robots context introduces. This work addresses the problem of applying these techniques to mobile robotics in a typical household scenario. We select two state-of-the-art object recognition methods, which are suitable to be adapted to mobile robots, and we evaluate them on a challenging dataset of typical household objects that caters to these requirements. The different advantages and drawbacks found for each method are highlighted, and some ideas for extending them are proposed. Evaluation is done comparing the number of detected objects and false positives for both approaches.