Abstract
In recent years, there has been a rapid increase in the number of service robots deployed for aiding people in their daily activities. Unfortunately, most of these robots require human input for training in order to do tasks in indoor environments. Successful domestic navigation often requires access to semantic information about the environment, which can be learned without human guidance. In this paper, we propose a set of DEDUCE-Diverse scEne Detection methods in Unseen Challenging Environments algorithms which incorporate deep fusion models derived from scene recognition systems and object detectors. The five methods described here have been evaluated on several popular recent image datasets, as well as real-world videos acquired through multiple mobile platforms.The final results show an improvement over the existing state-of-the-art visual place recognition systems
Authors
| Anwesan Pal Contextual Robotics Institute, UC San Diego a2pal [at] eng.ucsd.edu |
Carlos Nieto-Granda Contextual Robotics Institute, UC San Diego cnietogr [at] eng.ucsd.edu |
Henrik I. Christensen Contextual Robotics Institute, UC San Diego hichristensen [at] eng.ucsd.edu |
Paper
Video
Code/Data
Supplementary material including code and the videos of the different experiments are available at https://sites.google.com/eng.ucsd.edu/deduce.
Acknowledgement
The authors would like to thank Army Research Laboratory (ARL) W911NF-10-2-0016 Distributed and Collaborative Intelligent Systems and Technology (DCIST) Collaborative Technology Alliance for supporting this research.
Citation
[bibtex key=pal19:deduc]
Copyright
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