Dense SLAM: The Unidirectional Information Flow (UIF)

Abstract

This paper presents the concept of Unidirectional Information Flow (UIF) for state-space estimation. The notion of UIF is relevant to systems where the statevector is divided into two groups: (i) states that both give and receive information to and from the rest of the system and (ii) states that only receive information. This paper presents an application of the UIF concept to the problem of Simultaneous Localisation and Mapping (SLAM). Traditional SLAM maps are sparse and are built up of isolated landmarks observed in the environment. Although a dense representation may sometimes be desirable, the construction of a consistent dense map has previously not been possible for the incremental SLAM paradigm. It is also shown that by using the UIF, it is possible to obtain a more detailed environment representation (Dense SLAM) without increasing the computational burden of the algorithm and without loss of consistency. The same concept is also used to evaluate the error propagation inside the local dense maps. Experimental results are finally presented to validate the algorithms.