Sparsing of information matrix for practical application of a robot's SLAM

Abstract

Mobile robot could navigate in unknown environment autonomously with the help of simultaneous localization and mapping (SLAM). Recently, SLAM based on information matrix enjoys much popularity since it is naturally sparse. However, the computational burden related to information matrix balloons with respect to the increase of the mapped landmarks. In this paper, by considering the features of information matrix, we present a novel method which wipes off nearly half of the elements in information matrix. The errors that come from sparsification decrease apparently by loop-closure. Furthermore, the relationship between sparsification and SLAM accuracy is analyzed theoretically. A large scale simulation and experiment conducted on a real robot suggest that the technique is effective for a robot's SLAM in real-world applications.