Conservative Sparsification for efficient and consistent approximate estimation

This paper presents a new technique for sparsification of the information matrix of a multi-dimensional Gaussian distribution. We call this technique Conservative Sparsification (CS) and show that it produces estimates which are consistent with respect to an optimal filter. This technique was applied to the Simultaneous Localisation and Mapping (SLAM) problem, and compared with two existing sparsification approaches; the Sparse Extended Information Filter (SEIF) and the Data Discarding Sparse Extended Information Filter (DDSEIF). Simulation demonstrates that CS is a consistent approach and provides a tighter upper bound than existing conservative methods.