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Continuous Probabilistic SLAM Solved via Iterated Conditional Modes

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Abstract

This article proposes a simultaneous localization and mapping (SLAM) version with continuous probabilistic mapping (CP-SLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field (MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes (ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.

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Acknowledgements

This research was financed by Argentinean National Council for Scientific Research (CONICET) and the National University of San Juan (UNSJ) of Argentina. We also thank NVIDIA Corporation for their support.

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Authors and Affiliations

  1. Automatics Institute (INAUT), National University of San Juan - CONICET, San Juan, J5400ARL, Argentina

    J. Gimenez, A. Amicarelli, J. M. Toibero, F. di Sciascio & R. Carelli

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Correspondence to J. M. Toibero.

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Recommended by Associate Editor De Xu

J. Gimenez received the B. Sc. degree in mathematics from the National University of San Juan (UNSJ), Argentina in 2009, and the Ph. D. degree in mathematics from the National University of Córdoba (UNC), Argentina in 2014. Currently, he is an assistant researcher of the Argentinean National Council for Scientific Research (CONICET), and an adjunct professor in the Institute of Automatics, UNSJ-CONICET, Argentina.

His research interests include probabilistic and statistical implementations of robotics, such as SLAM algorithms.

A. Amicarelli received the Dipl-Ing degree in chemical engineering from the National University of San Juan, Argentina in 2000, and the Ph. D. degree in control systems from the Institute of Automatics (INAUT) of the same University in 2007. Her main works are related with state estimations of complex non-linear systems and with bioprocess control. She is adjunct researcher of the Argentinean National Council for Scientific Research (CONICET) from 2012.

Her research interests include systems modeling, process control and state estimation to control porpoises.

J. M. Toibero received the B. Eng. degree in electronic engineering from the Facultad Tecnológica Nacional (UTN-Paraná) of Argentina, Argentina in 2002, and the Ph. D. degree in control systems from the Institute of Automatics (INAUT), the National University of San Juan, Argentina in 2007. His main works are related to nonlinear control of robotic platforms and robotics applications in agriculture. He is with the National Council for Scientific and Technological Research (CONICET) of Argentina since 2011, actually he is adjunct researcher. He leads different technological projects and his cur- rent scientific research is at the Institute of Automatics (INAUT) of San Juan, Argentina.

His research interests include wheeled mobile robots, manipulators force/impedance, switched, hybrid, nonlinear control methods applied to automatic control and visual servoing.

F. di Sciascio received B. Sc. degree in electromechanical engineering with orientation in electronics from the National University of Buenos Aires (UBA), Argentina in 1986. He received the M. Sc. degree in engineering of control Systems in 1994, and the Ph. D. degree in engineering in 1997, from the Institute of Automatics (INAUT), National University of San Juan (UNSJ), Argentina. Since 1987, he is a professor and researcher at the INAUT and he is currently a full professor in charge of the subjects artificial intelligence complements and identification and adaptive control (degree in electronic engineering) in the Department of Electronics and Automation, at the same time, he is a professor of the postgraduate degree at the INAUT, Argentina.

His research interests include modelling, identification and estimation in dynamical systems, and technology developments in automatic process control.

R. Carelli received B. Sc. degree in engineering from the National University of San Juan, Argentina in 1976, and the Ph. D. degree in electrical engineering from the National University of Mexico (UNAM), Mexico in 1989. He is a full professor at the National University of San Juan and a senior researcher of the National Council for Scientific and Technical Research, Argentina. He is Director of the Automatics Institute, National University of San Juan, Argentina. He is a Senior Member of IEEE and a Member of Argentine Association of Automatic Control — International Federation of Automatic Control (AA-DECA-IFAC).

His research interests include robotics, manufacturing systems, adaptive control and artificial intelligence applied to automatic control.

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Gimenez, J., Amicarelli, A., Toibero, J.M. et al. Continuous Probabilistic SLAM Solved via Iterated Conditional Modes. Int. J. Autom. Comput. 16, 838–850 (2019). https://doi.org/10.1007/s11633-019-1186-7

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