The Autonomous City Explorer (ACE) project combines research from autonomous outdoor navigation and human-robot interaction. The ACE robot is capable of navigating unknown urban environments without the use of GPS data or prior map knowledge. It finds its way by interacting with pedestrians in a natural and intuitive way and building a topological representation of its surroundings. In a recent experiment the robot managed to successfully travel a 1.5 km distance from the campus of the Technische Universität München to Marienplatz, the central square of Munich.This article describes the principles and system components for navigation in urban environments, information retrieval through natural human-robot interaction, the construction of a suitable semantic representation as well as results from the field experiment.
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.
Estimating the human body pose is of great interest for many tasks, such as human robot interaction, people tracking and surveillance. During the recent years, several approaches have been presented, which still have weaknesses regarding occlusions or complex scenes. In this paper, we present a novel algorithm for human body pose estimation using any three-dimensional representation of the environment, like stereo vision. The presented algorithm is able to leave out body parts and is therefore able to deal with occluded body parts. In a first step, possible humans need to be detected, e.g. by using a skin color filter. A disparity map containing depth information is computed using a stereo matching algorithm. It leads to a three-dimensional representation of the scene. Starting with the detected skin parts, our algorithm segments this point cloud into smaller clusters. The possible matches are then verified, and the body pose is estimated using a kinematic human model with 28 degrees of freedom. As our algorithm is capable of dealing with arbitrary three-dimensional representations, it can easily be adapted to use a three-dimensional laser range finder instead of a stereo camera system.
Actuated car doors with more than one degree of freedom are a desirable means to boost the convenience of the access to cars. This paper outlines the problems connected with providing an intuitive, comfortable and safe operation of such doors. An advanced control system is proposed that overcomes these problems. First, a vision system for the monitoring of the workspace of the door is described. The data gathered is utilized by a collision avoidance method, which enables a safe operation of the car door. Second, a method for the support of the manual handling of the door is provided, which is based on predefined convenient paths for the specific kinematics of the door. Finally, the proposed control system is applied to a virtual car door, with successful results.
Zusammenfassung
Im Rahmen des “Autonomous City Explorer (ACE)” Projektes wurde ein Roboter entwickelt, der sich ohne die Verwendung von GPS oder vorherigem Kartenwissen in unbekannten Umgebungen zurechtfindet, indem er ähnlich wie ein Tourist Passanten nach dem Weg fragt. Während eines Experimentes legte ACE erfolgreich die 1,5 km lange Strecke vom Campus der Technische Universität München zum Marienplatz im Zentrum von München zurück. Dieses Pilotprojekt ist ein erster Schritt in Richtung Wissensbildung durch Mensch-Roboter-Interaktion zukünftiger humanoider Roboter.
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