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DTSTART:20210314T030000
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DTSTAMP:20211028T235047Z
UID:CA1D3410-870D-4C5C-92C6-2BC04F3B7747
DTSTART;TZID=America/Chicago:20211028T173000
DTEND;TZID=America/Chicago:20211028T184500
DESCRIPTION:At the core of most autonomous systems is a source of navigatio
 n data that provides position and orientation which often depends on a sin
 gle state estimation system. This single-point of failure reduces robustne
 ss and reliability. Prior work in SLAM often focusses on accuracy. In this
  talk we will explore several ideas to improve the robustness of SLAM syst
 ems while maintaining high accuracy and mapping resolution. We present the
  challenges\, as well as results of operating in dust\, fog\, high-resolut
 ion mapping\, and robust place recognition in a variety of situations with
  traditional as well as learning-based approaches to SLAM.\n\nSpeaker(s): 
 Dr. Sebastian Scherer\, \n\nLincoln\, Nebraska\, United States\, Virtual: 
 https://events.vtools.ieee.org/m/278963
LOCATION:Lincoln\, Nebraska\, United States\, Virtual: https://events.vtool
 s.ieee.org/m/278963
ORGANIZER:mvvrmkr@gmail.com
SEQUENCE:16
SUMMARY:Towards Robust Localization and Mapping in Challenging Conditions (
 Note:1 PDH (Professional Development Hour) will be provided for Profession
 al Engineers(PEs) upon the request)
URL;VALUE=URI:https://events.vtools.ieee.org/m/278963
X-ALT-DESC:Description: <br /><p>&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &n
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 &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; &n
 bsp\; &nbsp\; &nbsp\;At the core of most autonomous systems is a source of
  navigation data that provides position and orientation which often depend
 s on a single state estimation system. This single-point of failure reduce
 s robustness and reliability. Prior work in SLAM often focusses on accurac
 y. In this talk we will explore several ideas to improve the robustness of
  SLAM systems while maintaining high accuracy and mapping resolution. We p
 resent the challenges\, as well as results of operating in dust\, fog\, hi
 gh-resolution mapping\, and robust place recognition in a variety of situa
 tions with traditional as well as learning-based approaches to SLAM.</p>
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