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A new approach to solve SLAM challenges by relative map filter

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Abstract

In this paper we propose a new approach to solve some challenges in the simultaneous localization and mapping (SLAM) problem based on the relative map filter (RMF). This method assumes that the relative distances between the landmarks of relative map are estimated fully independently. This considerably reduces the computational complexity to average number of landmarks observed in each scan. To solve the ambiguity that may happen in finding the absolute locations of robot and landmarks, we have proposed two separate methods, the lowest position error (LPE) and minimum variance position estimator (MVPE). Another challenge in RMF is data association problem where we also propose an algorithm which works by using motion sensors without engaging in their cumulative error. To apply these methods, we switch successively between the absolute and relative positions of landmarks. Having a sufficient number of landmarks in the environment, our algorithm estimates the positions of robot and landmarks without using motion sensors and kinematics of robot. Motion sensors are only used for data association. The empirical studies on the proposed RMF-SLAM algorithm with the LPE or MVPE methods show a better accuracy in localization of robot and landmarks in comparison with the absolute map filter SLAM.

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Notes

  1. http://www-personal.acfr.usyd.edu.au/nebot/victoria_park.htm.

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Authors and Affiliations

  1. Artificial Intelligence Laboratory, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Sayed Farzad Bahreinian & Maziar Palhang

  2. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Mohammad Reza Taban

Authors
  1. Sayed Farzad Bahreinian
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  2. Maziar Palhang
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  3. Mohammad Reza Taban
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Correspondence to Sayed Farzad Bahreinian.

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Bahreinian, S.F., Palhang, M. & Taban, M.R. A new approach to solve SLAM challenges by relative map filter. Intel Serv Robotics 10, 271–286 (2017). https://doi.org/10.1007/s11370-017-0226-9

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