Modern robots collect a wealth of rich sensor data during their operation. While such data allows interesting analysis and sophisticated algorithms, it is simply infeasible to store all the data that is generated. However, collecting only samples of the data greatly minimizes the usefulness of the data. We present CC-LOG, a new logging system built on top of the widely-used Robot Operating System that uses a combination of classification and compression techniques to reduce storage requirements. Experiments using the Building-Wide Intelligence Robot, a mobile autonomous mobile platform capable of operat-ing for long periods of time in human-inhabited environments, showed that our proposed system can reduce storage requirements by more than an order of magnitude. Our results indicate that there is significant unrealized potential in optimizing infrastructure commonly used in robotics applications and research

PDF, HTML

In Proceedings of the 9th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage '17), Santa Clara, CA, July 2017.

@inproceedings{HotStorage2017-Gonzales, title={CC-Log: Drastically Reducing Storage Requirements for Robots Using Classification and Compression}, author={Santiago Gonzalez and Vijay Chidambaram and Jivko Sinapov and Peter Stone}, booktitle={Proceedings of the 9th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage '17)}, month={July}, address={Santa Clara, CA}, url="http://www.cs.utexas.edu/users/ai-lab?gonzales:usenix17", year={2017} }