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Carnegie Mellon Robotics Institute

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Abstract

In a series of recent papers Singh et al. have explored the idea of Simultaneous Localization and Mapping (SLAM) using range-only measurements. These measurements, obtained from radio or sonar sensors, come at irregular time intervals. In this report we explore the use of interpolation to generate data equally spaced in time, in order to improve the performance of SLAM algorithms. We test this idea on several (simulated and real) robot paths and two SLAM algorithms: an online Extended Kalman Filter (EKF) algorithm and an offline batch optimization algorithm.

Keywords

Localization, mapping, SLAM, optimization, Kalman filter

Notes

Associated Center(s) / Consortia: Field Robotics Center

Associated Project(s): EMBER

Number of pages: 23

Text Reference

Athanasios Kehagias, Joseph Djugash, and Sanjiv Singh, "Range-only SLAM with Interpolated Range Data," tech. report CMU-RI-TR-06-26, Robotics Institute, Carnegie Mellon University, May, 2006

BibTeX Reference

@techreport{Kehagias_2006_5430,
   author = "Athanasios Kehagias and Joseph Djugash and Sanjiv Singh",
   title = "Range-only SLAM with Interpolated Range Data",
   booktitle = "",
   institution = "Robotics Institute",
   month = "May",
   year = "2006",
   number= "CMU-RI-TR-06-26",
   address= "Pittsburgh, PA",
}

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