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%.