Comparison of Indoor Robot Localization Techniques in the Absence of GPS

Citation

Regis Vincent, Benson Limketkai, and Michael Eriksen “Comparison of indoor robot localization techniques in the absence of GPS”, Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76641Z (29 April 2010); https://doi.org/10.1117/12.849593

Abstract

When available, GPS is the quick and easy solution to localizing a robot. However, because it is often not available (e.g. indoors) or not reliable enough, other techniques, using laser range finders or cameras have been developed that offer better performance. For 2D localization,lLaser range finders are far more precise and easier to work with than cameras. We report here on the performance of several implementations of the main class of localization algorithms that use a laser, Simultaneous Localization And Mapping (SLAM) on the RAWSEEDS benchmark. SRI International’s SLAM system has an RMS error in XY of 0.32m (0.22%). This is the best reported performance on this benchmark.


Read more from SRI

  • Banner and attendees at the IEEE Hard Tech Venture Summit

    Cultivating hard tech startups that scale

    IEEE’s Hard Tech Venture Summit convened innovators at SRI to refine strategies and build new networks.

  • Patient going into a MRI

    Bringing surgical tools inside the MRI

    Drawing on SRI’s unique innovation ecosystem, the startup Medical Devices Corner is seeking to improve cancer surgery by advancing MRI-safe teleoperation.

  • Christopher Mims and Susan Patrick

    PARC Forum: How to AI

    The Wall Street Journal tech columnist Christopher Mims and SRI Education’s Susan Patrick discuss how AI can strengthen human agency.