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Quirin Mühlbauer

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

ICRA Conference 2009 Conference Paper

Navigation through urban environments by visual perception and interaction

  • Quirin Mühlbauer
  • Stefan Sosnowski
  • Tingting Xu
  • Tianguang Zhang
  • Kolja Kühnlenz
  • Martin Buss

In the autonomous city explorer (ACE) project a mobile robot is developed, which is capable of finding its way to a given destination in an unknown urban environment. An exemplary mission is to find the way from our institute to the Marienplatz, a public place in the center of Munich, without any prior knowledge or GPS information. Inspired by the behavior of humans in unknown environments, ACE must find its way by asking pedestrians. The route is about 1. 5 kilometers far and includes heavily traveled roads and crowded public places. In order to navigate safely in an unknown urban environment, some challenges arise for the vision system. Robust human detection, tracking and the estimation of human body poses is essential for natural interaction with pedestrians. Furthermore, the robot needs to be able to detect sidewalk and crossroads. A visual odometry system is used to support the conventional navigation. Outdoor experiments were conducted twice successfully. After about 5 hours and interacting with 25 and 38 persons respectively, ACE arrived the Marienplatz. This paper describes both, an architecture of the vision system used for ACE and the algorithms used to deal with the described challenges.

ICRA Conference 2009 Conference Paper

The Autonomous City Explorer project

  • Andrea Maria Bauer
  • Klaas Klasing
  • Tingting Xu
  • Stefan Sosnowski
  • Georgios Lidoris
  • Quirin Mühlbauer
  • Tianguang Zhang
  • Florian Rohrmüller

This video presents the Autonomous City Explorer (ACE) project. Its goal was to create a robot capable of navigating unknown urban environments without the use of GPS data or prior map knowledge. The robot had to find its way solely by interacting with pedestrians and building a topological representation of its surroundings. This video outlines the necessary ingredients for successful low-level navigation on sidewalks, information retrieval from pedestrians as well as the construction of a semantic representation of an urban environment. A system architecture for outdoor localization, traversability assessment, path planning, behavior selection and topological abstraction in urban environments is presented.

v2026.09.13