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Inertial Navigation and Mapping for Autonomous Vehicles
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
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.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1623
Keyword [sv]
SLAM, Inertial Navigation, Filtering, Smoothing, Cameras, Optimisation, Autonomous
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-110373DOI: 10.3384/diss.diva-110373ISBN: 9789175192338 (print)OAI: oai:DiVA.org:liu-110373DiVA: diva2:744998
Public defence
2014-10-17, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Projects
LINK-SIC
Funder
VINNOVA
Available from: 2014-09-17 Created: 2014-09-09 Last updated: 2017-01-19Bibliographically approved
List of papers
1. Silent Localization of Underwater Sensors Using Magnetometers
Open this publication in new window or tab >>Silent Localization of Underwater Sensors Using Magnetometers
2010 (English)In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, Vol. 2010, no 1Article in journal (Refereed) Published
Abstract [en]

Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magnetic dipole to silently localize the sensors. The ferromagnetic field created by the dipole is measured by the magnetometers and is used to localize the sensors. The trajectory of the vessel and the sensor positions are estimated simultaneously using an Extended Kalman Filter (EKF). Simulations show that the sensors can be accurately positioned using magnetometers.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2010
Keyword
Underwater sensor localization, Sensor network, Magnetometers, SLAM
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-53589 (URN)10.1155/2010/709318 (DOI)000274966500001 ()
Projects
MOVIIICADICSLINK-SIC
Available from: 2010-01-25 Created: 2010-01-25 Last updated: 2014-09-30Bibliographically approved
2. A Nonlinear Least-Squares Approach to the SLAM Problem
Open this publication in new window or tab >>A Nonlinear Least-Squares Approach to the SLAM Problem
2011 (English)In: Proceedings of the 18th IFAC World Congress, 2011: World Congress, Volume # 18, Part 1 / [ed] Sergio Bittanti, Angelo Cenedese and Sandro Zampieri, IFAC Papers Online, 2011, 4759-4764 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present a solution to the simultaneous localisation and mapping (SLAM) problem using a camera and inertial sensors. Our approach is based on the maximum a posteriori (MAP) estimate of the complete SLAM problem. The resulting problem is posed in a nonlinear least-squares framework which we solve with the Gauss-Newton method. The proposed algorithm is evaluated on experimental data using a sensor platform mounted on an industrial robot. In this way, accurate ground truth is available, and the results are encouraging.

Place, publisher, year, edition, pages
IFAC Papers Online, 2011
Keyword
Inertial measurement units, Cameras, Smoothing, Dynamic systems, State estimation
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-68857 (URN)10.3182/20110828-6-IT-1002.02042 (DOI)978-3-902661-93-7 (ISBN)
Conference
The 18th IFAC World Congress, 2011, August 28th to Friday September 2nd, Milano, Italy
Available from: 2011-06-08 Created: 2011-06-08 Last updated: 2016-05-03Bibliographically approved
3. Modeling and Sensor Fusion of a Remotely Operated Underwater Vehicle
Open this publication in new window or tab >>Modeling and Sensor Fusion of a Remotely Operated Underwater Vehicle
2012 (English)In: Proceedings of the 15th International Conference on Information Fusion (FUSION), 2012, IEEE , 2012, 947-954 p.Conference paper, Published paper (Refereed)
Abstract [en]

We compare dead-reckoning of underwater vehicles based on inertial sensors and kinematic models on one hand, and control inputs and hydrodynamic model on the other hand. Both can be used in an inertial navigation system to provide relative motion and absolute orientation of the vehicle. The combination of them is particularly useful for robust navigation in the case of missing data from the crucial doppler log speedometer. As a concrete result, we demonstrate that the performance critical doppler log can be replaced with longitudinal dynamics in the case of missing data, based on field test data of a remotely operated vehicle.

Place, publisher, year, edition, pages
IEEE, 2012
Keyword
autonomous underwater vehicles, hydrodynamics, inertial navigation, kinematics, sensor fusion
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-97490 (URN)978-0-9824438-4-2 (ISBN)978-1-4673-0417-7 (ISBN)
Conference
15th International Conference on Information Fusion (FUSION), 9-12 July 2012, Singapore
Projects
LINK-SIC
Available from: 2013-09-13 Created: 2013-09-13 Last updated: 2014-09-17Bibliographically approved
4. Initialisation and Estimation Methods for Batch Optimisation of Inertial/Visual SLAM
Open this publication in new window or tab >>Initialisation and Estimation Methods for Batch Optimisation of Inertial/Visual SLAM
2013 (English)Report (Other academic)
Abstract [en]

Simultaneous Localisation and Mapping (SLAM) denotes the problem of jointly localizing a moving platform and mapping the environment. This work studies the SLAM problem using a combination of inertial sensors, measuring the platform's accelerations and angular velocities, and a monocular camera observing the environment. We formulate the SLAM problem on a nonlinear least squares (NLS) batch form, whose solution provides a smoothed estimate of the motion and map. The NLS problem is highly nonconvex in practice, so a good initial estimate is required. We propose a multi-stage iterative procedure, that utilises the fact that the SLAM problem is linear if the platform's rotations are known. The map is initialised with camera feature detections only, by utilising feature tracking and clustering of  feature tracks. In this way, loop closures are automatically detected. The initialization method and subsequent NLS refinement is demonstrated on both simulated and real data.

Publisher
15 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3065
Keyword
Simultaneous localisation and mapping, optimisation, inertial measurement unit, monocular camera
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-97278 (URN)LiTH-ISY-R-3065 (ISRN)
Available from: 2013-09-09 Created: 2013-09-05 Last updated: 2017-01-19Bibliographically approved
5. EM-SLAM with Inertial/Visual Applications
Open this publication in new window or tab >>EM-SLAM with Inertial/Visual Applications
2017 (English)In: IEEE Transactions on Aerospace and Electronic Systems, ISSN 0018-9251, E-ISSN 1557-9603, Vol. 53, no 1, 273-285 p.Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keyword
SLAM, Expectation-Maximisation, Sensor Fu- sion, Computer Vision, Inertial Sensors
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-110371 (URN)10.1109/TAES.2017.2650118 (DOI)000399934000022 ()
Note

Funding agencies: Vinnova Industry Excellence Center LINK-SIC

Available from: 2014-09-09 Created: 2014-09-09 Last updated: 2017-05-18Bibliographically approved

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