UCLA

This paper introduces the use of compressed sens- ing for autonomous robots performing environmental mapping in order to reduce data collection, storage, and transmission requirements. A prototype robot sends data collected over adaptively updated straight-line paths to a server, which reconstructs an image of the environment variable using Split- Bregman iteration. The amount of data collected is only 10% of the amount of data in the final map, yet the relative error is only 20%.