A Generic Approach to Self-localization and Mapping of Mobile Robots Without Using a Kinematic Model

Patrick Kesper, Lars Berscheid, Florentin Wörgötter, Poramate Manoonpong

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper a generic approach to the SLAM (Simultaneous Localization and Mapping) problem is proposed. The approach is based on a probabilistic SLAM algorithm and employs only two portable sensors, an inertial measurement unit (IMU) and a laser range finder (LRF) to estimate the state and environment of a robot. Scan-matching is applied to compensate for noisy IMU measurements. This approach does not require any robot-specific characteristics, e.g. wheel encoders or kinematic models. In principle, this minimal sensory setup can be mounted on different robot systems without major modifications to the underlying algorithms. The sensory setup with the probabilistic algorithm is tested in real-world experiments on two different kinds of robots: a simple two-wheeled robot and the six-legged hexapod AMOSII. The obtained results indicate a successful implementation of the approach and confirm its generic nature. On both robots, the SLAM problem can be solved with reasonable accuracy.
Original languageEnglish
Title of host publicationTowards Autonomous Robotic Systems : 16th Annual Conference, TAROS 2015, Liverpool, UK, September 8-10, 2015, Proceedings
EditorsClare Dixon, Karl Tuyls
PublisherSpringer
Publication date2015
Pages136-142
ISBN (Print)978-3-319-22415-2
ISBN (Electronic)978-3-319-22416-9
DOIs
Publication statusPublished - 2015
Event16th Annual Conference Towards Autonomous Robotic Systems - Liverpool, United Kingdom
Duration: 8. Sep 201510. Sep 2015

Conference

Conference16th Annual Conference Towards Autonomous Robotic Systems
Country/TerritoryUnited Kingdom
CityLiverpool
Period08/09/201510/09/2015
SeriesLecture Notes in Computer Science
Volume9287
ISSN0302-9743

Keywords

  • mobile robots
  • Localization
  • mapping
  • SLAM
  • Laser range finder
  • Hexapod robot
  • Inertial measurement unit
  • Mobile robots
  • Probabilistic robotics

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