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Pose Selection and Feedback Methods in Tandem Combinations of Particle Filters with Scan-Matching for 2D Mobile Robot Localisation

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Abstract

Robot localisation is predominantly resolved via parametric or non-parametric probabilistic methods. The particle filter, the most common non-parametric approach, is a Monte Carlo Localisation (MCL) method that is extensively used in robot localisation, as it can represent arbitrary probabilistic distributions, in contrast to Kalman filters, which is the standard parametric representation. In particle filters, a weight is internally assigned to each particle, and this weight serves as an indicator of a particle’s estimation certainty. Their output, the tracked object’s pose estimate, is implicitly assumed to be the weighted average pose of all particles; however, we argue that disregarding low-weight particles from this averaging process may yield an increase in accuracy. Furthermore, we argue that scan-matching, treated as a prosthesis of (or, put differently, fit in tandem with) a particle filter, can also lead to better accuracy. Moreover, we study the effect of feeding back this improved estimate to MCL, and introduce a feedback method that outperforms current state-of-the-art feedback approaches in accuracy and robustness, while alleviating their drawbacks. In the process of formulating these hypotheses we construct a localisation pipeline that admits configurations that are a superset of state-of-the-art configurations of tandem combinations of particle filters with scan-matching. The above hypotheses are tested in two simulated environments and results support our argumentation.

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Acknowledgments

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH CREATE INNOVATE (project code:T1EDK-03032).

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece

    Alexandros Filotheou, Emmanouil Tsardoulias, Antonis Dimitriou, Andreas Symeonidis & Loukas Petrou

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  1. Alexandros Filotheou
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Correspondence to Alexandros Filotheou.

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Appendix: A

Appendix: A

This section features magnified versions of the figures found in Sections 7.1 and 7.2 for the use of drawing clearer comparisons between results of different selection (Section A) and feedback (Section B) methods.

1.1 A Focused Results of Tests on Selection Methods

Figures 11 and 12 comprise the distributions of the mean 2-norm total pose error of open-loop MCL per selection method tested across N = 100 simulations, as seen in Figs. 6 and 7 respectively, focused here for clarity of comparison purposes.

Fig. 11
figure 11

The distribution of the mean 2-norm total pose error of open-loop-MCL (to the left of each indicated selection method) and of the compound system’s (to the right of each indicated selection method) across N = 100 simulations according to population selection method in environment CORRIDOR, focused for clarity of comparison

Full size image
Fig. 12
figure 12

The distribution of the mean 2-norm total pose error of open-loop-MCL (to the left of each indicated selection method) and of the compound system’s (to the right of each indicated selection method) across N = 100 simulations according to population selection method in environment WAREHOUSE, focused for clarity of comparison

Full size image

1.2 B Focused Results of Tests on Feedback Methods

Figures 13 and 14 comprise the distributions of the mean 2-norm total pose error of open-loop MCL per feedback method tested across N = 100 simulations, as seen in Figs. 8 and 9 respectively, focused here for clarity of comparison purposes.

Fig. 13
figure 13

The distribution of the mean 2-norm total pose error of MCL (to the left of each indicated feedback method) and of the compound system’s (to the right of each indicated feedback method) across N = 100 simulations according to feedback method in environment CORRIDOR, focused for clarity of comparison

Full size image
Fig. 14
figure 14

The distribution of the mean 2-norm total pose error of MCL (to the left of each indicated feedback method) and of the compound system’s (to the right of each indicated feedback method) across N = 100 simulations according to feedback method in environment WAREHOUSE, focused for clarity of comparison

Full size image

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Filotheou, A., Tsardoulias, E., Dimitriou, A. et al. Pose Selection and Feedback Methods in Tandem Combinations of Particle Filters with Scan-Matching for 2D Mobile Robot Localisation. J Intell Robot Syst 100, 925–944 (2020). https://doi.org/10.1007/s10846-020-01253-6

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