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Single and Multi Camera Simultaneous Localization and Mapping Using the Extended Kalman Filter

On the Different Parameterizations for 3D Point Features

  • Published:
Journal of Mathematical Modelling and Algorithms in Operations Research

Abstract

Simultaneous Localization and Mapping (SLAM) has received quite a lot of attention in the last decades because of its relevance for many applications centered on a mobile observer, such as service robotics and intelligent transportation systems. This paper focuses on the use of recursive Bayesian filtering, as implemented by the Extendend Kalman Filter (EKF), to face the Visual SLAM problem, i.e., when using data from visual sources. In Monocular SLAM, which uses a single camera as unique source of information, it is not possible to directly estimate the depth of a feature from a single image. To handle the severely non-normal distribution representing such uncertainty, inverse parametrizations were developed, capable to deal with such uncertainty and still relying on Gaussian variables. In the paper, after an introduction to EKF-SLAM, we provide a review of different inverse parametrizations, and we introduce a novel proposal, the Framed Inverse Depth (FID) parametrization, which, in terms of consistency, performs similarly to state of the art Monocular SLAM parametrizations, but at a reduced computational cost. All these parametrizations can be used in a stereo and multi camera setting too. An extensive analysis is presented for both Monocular and stereo SLAM, for a simulated environment widely used in the literature as well as on a widely used real dataset.

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Authors and Affiliations

  1. Politecnico di Milano (DEIB), via Ponzio 34/5, 20133, Milano, Italy

    Simone Ceriani & Matteo Matteucci

  2. InfoSolution S.p.A., via della Burrona 51, 20090, Vimodrone (Mi), Italy

    Daniele Marzorati

  3. Università degli Studi di Milano - Bicocca (DISCo), Building U14, viale Sarca 336, 20126, Milano, Italy

    Domenico G. Sorrenti

Authors
  1. Simone Ceriani
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  2. Daniele Marzorati
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  3. Matteo Matteucci
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  4. Domenico G. Sorrenti
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Corresponding author

Correspondence to Domenico G. Sorrenti.

Additional information

This work has been partially supported by the Italian Ministry of University and Research (MIUR) through the PRIN 2009 grant “ROAMFREE: Robust Odometry Applying Multi-sensor Fusion to Reduce Estimation Errors”.

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Ceriani, S., Marzorati, D., Matteucci, M. et al. Single and Multi Camera Simultaneous Localization and Mapping Using the Extended Kalman Filter. J Math Model Algor 13, 23–57 (2014). https://doi.org/10.1007/s10852-013-9219-7

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  • DOI: https://doi.org/10.1007/s10852-013-9219-7

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