Odometry Based on Auto-Calibrating Inertial Measurement Unit Attached to the Feet
| Autor: | Olivier Stasse, Dinesh Atchuthan, Joan Sola, Nicolas Mansard, Angel Santamaria-Navarro |
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| Přispěvatelé: | Équipe Mouvement des Systèmes Anthropomorphes (LAAS-GEPETTO), Laboratoire d'analyse et d'architecture des systèmes (LAAS), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT), Institut de Robòtica i Informàtica Industrial (IRI), Universitat Politècnica de Catalunya [Barcelona] (UPC)-Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Project EB-SLAM (DPI2017-89564-P), Mara de Maeztu Seal of Excellence to IRI MDM-2016-0656, KIOS Research and Innovation Center of Excellence, ANR-16-CE33-0003,LOCO3D,Locomotion en environnement complexe(2016), European Project: 608849,EC:FP7:ICT,FP7-2013-NMP-ICT-FOF,EUROC(2014), European Project: 780684,H2020,MEMMO(2018), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universitat Politècnica de Catalunya [Barcelona] (UPC) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Pedestrian navigation Computer science Context (language use) 02 engineering and technology Accelerometer IMU bias estimation law.invention [SPI.AUTO]Engineering Sciences [physics]/Automatic Pedestrian dead reckoning 020901 industrial engineering & automation Odometry law Inertial measurement unit 11. Sustainability 0202 electrical engineering electronic engineering information engineering [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Computer vision Graphical model Factor Graph Sensor fusion business.industry 020208 electrical & electronic engineering Gyroscope IMU Graph (abstract data type) Artificial intelligence business |
| Zdroj: | European Control Conference (ECC 2018) European Control Conference (ECC 2018), KIOS Research and Innovation Center of Excellence, Jun 2018, Limassol, Cyprus. 8p ECC HAL 2018 European Control Conference (ECC) IEEE Humanoids |
| Popis: | International audience; Location of pedestrian in indoor environment remains an open problem. A cheap and reliable sensor in this context is the inertial measurement units (IMU), carried by the pedestrian while he/she is walking. However, due to the bias of both the accelerometer and the gyroscope, integrating directly the inertial measurements leads to tremendous drift, as the state of the system (position, orientation, velocity, bias) is not fully observable. In this paper, we consider the specific case where an IMU is attached to one of the pedestrian's feet. We exploit specific prior knowledges (i.e. the fact that the foot lands at zero velocity on a horizontal plane) in order to make the full state of the IMU observable. The inertial measurements and these prior knowledges are gathered in a graphical model (a factor graph), and are exploited to build a maximum-likelihood estimator. The technical difficulty is to handle the size of the graph such that it is tractable in a limited time window, that we do by relying on the pre-integration technique. In that existing framework, our contributions are to reformulate the pre-integration method using quaternions while giving a simpler algebraic formulation, and to apply this method for estimating the human foot-pose during walking. We validate these concepts on several long-range trajectories capture with human subject and compare the results with ground-truth measurements (coming from a motion capture system) and previous results of the state of the art. |
| Databáze: | OpenAIRE |
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