loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Timo Korthals ; Marvin Barther ; Thomas Schöpping ; Stefan Herbrechtsmeier and Ulrich Rückert

Affiliation: Bielefeld University, Germany

ISBN: 978-989-758-198-4

Keyword(s): Occupancy Grid Mapping, Inverse Sensor Model, Inverse Particle Filter, Uncertain Range Sensors.

Related Ontology Subjects/Areas/Topics: Adaptive Signal Processing and Control ; Informatics in Control, Automation and Robotics ; Mobile Robots and Autonomous Systems ; Perception and Awareness ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control

Abstract: A huge number of techniques for detecting and mapping obstacles based on LIDAR and SONAR exist, though not taking approximative sensors with high levels of uncertainty into consideration. The proposed mapping method in this article is undertaken by detecting surfaces and approximating objects by distance using sensors with high localization ambiguity. Detection is based on an Inverse Particle Filter, which uses readings from single or multiple sensors as well as a robot’s motion. This contribution describes the extension of the Sequential Importance Resampling filter to detect objects based on an analytical sensor model and embedding into Occupancy Grid Maps. The approach has been applied to the autonomous mini robot AMiRo in a distributed way. There were promising results for its low-power, low-cost proximity sensors in various real life mapping scenarios, which outperform the standard Inverse Sensor Model approach.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.238.194.166

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Korthals, T.; Barther, M.; Schöpping, T.; Herbrechtsmeier, S. and Rückert, U. (2016). Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters.In Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-758-198-4, pages 192-200. DOI: 10.5220/0005960001920200

@conference{icinco16,
author={Timo Korthals. and Marvin Barther. and Thomas Schöpping. and Stefan Herbrechtsmeier. and Ulrich Rückert.},
title={Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters},
booktitle={Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2016},
pages={192-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005960001920200},
isbn={978-989-758-198-4},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters
SN - 978-989-758-198-4
AU - Korthals, T.
AU - Barther, M.
AU - Schöpping, T.
AU - Herbrechtsmeier, S.
AU - Rückert, U.
PY - 2016
SP - 192
EP - 200
DO - 10.5220/0005960001920200

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.