Improving the real-time efficiency of inertial SLAM and understanding its observability

This paper addresses the computational complexity associated with 6-DoF inertial SLAM by recasting the filter into an indirect or complimentary structure. By doing so the standard non-linear inertial SLAM algorithm is piecewise linearised, providing two significant breakthroughs. Firstly, through the process of linearisation, the filter estimates the errors in the inertial states (errors in position, velocity and attitude) as opposed to the inertial states themselves, thus significantly reducing the amount of prediction updates required and hence the computational burden generally associated with inertial SLAM. Secondly, due to the piecewise linear nature of the underlying model, a new level of understanding of the observability of SLAM can be carried out through the use of the stripped observability matrix. This paper focuses on the 6-DoF implementation of SLAM in an airborne vehicle. Results illustrating that the new filter structure can efficiently estimate the errors and provide a navigation solution which is comparable to the standard implementation with significantly less computational cost is provided, along with theoretical results on the observability of 6-DoF SLAM.