Pose Selection and Feedback Methods in Tandem Combinations of Particle Filters with Scan-Matching for 2D Mobile Robot Lo
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Pose Selection and Feedback Methods in Tandem Combinations of Particle Filters with Scan-Matching for 2D Mobile Robot Localisation Alexandros Filotheou1
· Emmanouil Tsardoulias1 · Antonis Dimitriou1 · Andreas Symeonidis1 · Loukas Petrou1
Received: 25 October 2019 / Accepted: 28 August 2020 © Springer Nature B.V. 2020
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. Keywords Robot Localisation · Particle Filters · Scan-matching
1 Introduction Mobile robot localisation is a well-studied field in robotics, and several diverse approaches to localisation have been proposed in the past. Probabilistic methods [1] have been applied to the task of localisation and proved their success Alexandros Filotheou
[email protected] Emmanouil Tsardoulias [email protected] Antonis Dimitriou [email protected] Andreas Symeonidis [email protected] Loukas Petrou [email protected] 1
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
and robustness to sensor noise, map discrepancies with regard to a robot’s operating environment, motion model discrepancies with regard to the true kinematics of the robot, and pose uncertainty. As for sensors, laser range finders are popular devices employed in robot localisation due to their measurement accuracy, real-time operability, and virtually no need for preprocessing. Particle filters [2] comprise a probabilistic method for tackling the robot localisation problem, arising from the need for a robot to be more flexible in its pose and orientation belief. Instead
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