Maximally informative statistics for localization and mapping

Abstract

This paper presents an algorithm for simultaneous localization and mapping for a mobile robot using monocular vision and odometry. The approach uses variable state dimension filtering (VSDF) framework to combine aspects of extended Kalman filtering (EKF) and nonlinear batch optimization. This paper describes two primary improvements to the VSDF. The first is to use the maximally informative statistics criterion to derive an interpolation scheme for linearization in recursive filtering. The interpolation is based on fitting a set of deterministic samples rather than using analytic Jacobians. The second improvement is to replace the inverse covariance matrix-in the VSDF with its Cholesky factor to improve the computational complexity. Results of applying the filter to the localization and mapping are presented.