A compression method for 3-D laser range scans of indoor environments based on compressive sensing

Abstract

Modeling and representing 3-D environments require the transmission and storage of vast amount of measurements that need to be compressed efficiently. We propose a novel compression technique based on compressive sensing for 3-D range measurements that are found to be correlated with each other. The main issue here is finding a highly sparse representation of the range measurements, since they do not have highly sparse representations in common domains, such as the frequency domain. To solve this problem, we generate sparse innovations between consecutive range measurements along the axis of the sensor's motion. We obtain highly sparse innovations compared with other possible ones generated by estimation and filtering. Being a lossy technique, the proposed method performs reasonably well compared with widely used compression techniques.