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Rao-Blackwellized Particle Smoothing for Occupancy-Grid Based SLAM Using Low-Cost Sensors

Berntorp, Karl LU and Nordh, Jerker LU (2014) 19th IFAC World Congress, 2014
Abstract
We approach the simultaneous localization and mapping problem by using an ultrasound sensor and wheel encoders on a mobile robot. The measurements are modeled to yield a conditionally linear model for all the map states. Moreover, we implement a Rao-Blackwellized particle smoother (RBPS) for jointly estimating the position of the robot and the map. The method is applied and successfully verified by experiments on a small Lego robot where ground truth was obtained by the use of a VICON real-time positioning system. The results show that the RBPS contributes with more robust estimates at the cost of computational complexity and memory usage.
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type
Contribution to conference
publication status
published
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conference name
19th IFAC World Congress, 2014
external identifiers
  • Scopus:84929815822
language
English
LU publication?
yes
id
260dc580-5aa8-426b-a4e8-05b879672f92 (old id 4316373)
date added to LUP
2014-02-14 12:29:36
date last changed
2016-10-13 04:56:31
@misc{260dc580-5aa8-426b-a4e8-05b879672f92,
  abstract     = {We approach the simultaneous localization and mapping problem by using an ultrasound sensor and wheel encoders on a mobile robot. The measurements are modeled to yield a conditionally linear model for all the map states. Moreover, we implement a Rao-Blackwellized particle smoother (RBPS) for jointly estimating the position of the robot and the map. The method is applied and successfully verified by experiments on a small Lego robot where ground truth was obtained by the use of a VICON real-time positioning system. The results show that the RBPS contributes with more robust estimates at the cost of computational complexity and memory usage.},
  author       = {Berntorp, Karl and Nordh, Jerker},
  language     = {eng},
  title        = {Rao-Blackwellized Particle Smoothing for Occupancy-Grid Based SLAM Using Low-Cost Sensors},
  year         = {2014},
}