Compressive mobile sensing for robotic mapping

Abstract

Compressive sensing is an emerging research field based on the fact that a small number of linear measurements can recover a sparse signal under an orthogonal basis without losing any useful information. Using this approach, the signal can be recovered by a rate that is much lower than the requirement from the well-known Shannon sampling theory. In this paper, we propose a novel approach named compressive mobile sensing, which implements compressive sensing technique on a mobile sensor. This approach employs one mobile sensor or multiple sensors to reconstruct the sensing fields in an efficient way. Moreover, a special measurement process has been built under the constraint of the mobile sensors. It is also presented the simulation and experimental results of a robotic mapping problem using compressive mobile sensing.