EM-SLAM with Inertial/Visual Applications
(English)Manuscript (preprint) (Other academic)
The general Simultaneous Localisation and Mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with building a map of the local environment. There are essentially three classes of algorithms. EKF- SLAM and FastSLAM solve the problem on-line, while Nonlinear Least Squares (NLS) is a batch method. All of them scales badly with either the state dimension, the map dimension or the batch length. We investigate the EM algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively, hence it scales much better with dimensions. The iterations switch between state estimation, where we propose an Extended Rauch-Tung-Striebel smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. It is demonstrated to produce lower RMSE than with a standard Levenberg-Marquardt solver of NLS problem, at a computational cost that increases considerably slower.
SLAM, Expectation-Maximisation, Sensor Fu- sion, Computer Vision, Inertial Sensors
IdentifiersURN: urn:nbn:se:liu:diva-110371OAI: oai:DiVA.org:liu-110371DiVA: diva2:744965