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EM-SLAM with Inertial/Visual Applications
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

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. 

Keyword [en]
SLAM, Expectation-Maximisation, Sensor Fu- sion, Computer Vision, Inertial Sensors
National Category
URN: urn:nbn:se:liu:diva-110371OAI: diva2:744965
Available from: 2014-09-09 Created: 2014-09-09 Last updated: 2014-09-17
In thesis
1. Inertial Navigation and Mapping for Autonomous Vehicles
Open this publication in new window or tab >>Inertial Navigation and Mapping for Autonomous Vehicles
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Navigation and mapping in unknown environments is an important building block for increased autonomy of unmanned vehicles, since external positioning systems can be susceptible to interference or simply being inaccessible. Navigation and mapping require signal processing of vehicle sensor data to estimate motion relative to the surrounding environment and to simultaneously estimate various properties of the surrounding environment. Physical models of sensors, vehicle motion and external influences are used in conjunction with statistically motivated methods to solve these problems. This thesis mainly addresses three navigation and mapping problems which are described below.

We study how a vessel with known magnetic signature and a sensor network with magnetometers can be used to determine the sensor positions and simultaneously determine the vessel's route in an extended Kalman filter (EKF). This is a so-called simultaneous localisation and mapping (SLAM) problem with a reversed measurement relationship.

Previously determined hydrodynamic models for a remotely operated vehicle (ROV) are used together with the vessel's sensors to improve the navigation performance using an EKF. Data from sea trials is used to evaluate the system and the results show that especially the linear velocity relative to the water can be accurately determined.

The third problem addressed is SLAM with inertial sensors, accelerometers and gyroscopes, and an optical camera contained in a single sensor unit. This problem spans over three publications.

We study how a SLAM estimate, consisting of a point cloud map, the sensor unit's three dimensional trajectory and speed as well as its orientation, can be improved by solving a nonlinear least-squares (NLS) problem. NLS minimisation of the predicted motion error and the predicted point cloud coordinates given all camera measurements is initialised using EKF-SLAM.

We show how NLS-SLAM can be initialised as a sequence of almost uncoupled problems with simple and often linear solutions. It also scales much better to larger data sets than EKF-SLAM. The results obtained using NLS-SLAM are significantly better using the proposed initialisation method than if started from arbitrary points. A SLAM formulation using the expectation maximisation (EM) algorithm is proposed. EM splits the original problem into two simpler problems and solves them iteratively. Here the platform motion is one problem and the landmark map is the other. The first problem is solved using an extended Rauch-Tung-Striebel smoother while the second problem is solved with a quasi-Newton method. The results using EM-SLAM are better than NLS-SLAM both in terms of accuracy and complexity.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2014. 77 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1623
SLAM, Inertial Navigation, Filtering, Smoothing, Cameras, Optimisation, Autonomous
National Category
Control Engineering
urn:nbn:se:liu:diva-110373 (URN)10.3384/diss.diva-110373 (DOI)978-91-7519-233-8 (print) (ISBN)
Public defence
2014-10-17, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Available from: 2014-09-17 Created: 2014-09-09 Last updated: 2014-09-17Bibliographically approved

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Sjanic, ZoranSkoglund, Martin A.Gustafsson, Fredrik
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