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 Universitat Munchen 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.

The Autonomous City Explorer: Towards Natural Human-Robot Interaction in Urban Environments

Systems and methods for image data fusion include providing first and second sets of image data corresponding to an imaged first and second scene respectively The scenes at least partially overlap in an overlap region, defining a first collection of overlap image data as part of the first set of image data, and a second collection of overlap image data as part of the second set of image data The second collection of overlap image data is represented as a plurality of image data subsets such that each of the subsets is based on at least one characteristic of the second collection, and each subset spans the overlap region A fused set of image data is produced by an image processor, by modifying the first collection of overlap image data based on at least a selected one of, but less than all of, the image data subsets

Image data fusion systems and methods

This paper deals with the concept and implementation of a multi-purpose vision platform. In robotics, numerous applications require perception. A multi-purpose vision platform suited for object recognition, cultural heritage preservation and visual servoing at the same time is missing. In this work, we draw attention to the design principles for such a vision platform. We present its implementation, the 3D-modeller. In specifying and combining multiple sensors, laser-range scanner, laser-stripe profiler and stereo vision, we derive the required mechanical and electrical hardware design. The concepts for synchronization and communication round offs our approach. Precision and frame rate are presented. We illustrate the versatility of the 3D-modeller by addressing four applications: 3D-modeling, exploration, tracking and object recognition. Due to its low weight and generic mechanical interface, it can be mounted on industrial robots, humanoids, or free-handed as well. The 3D-modeller is flexibly applicable, not only in research but also in industry, especially in small batch assembly.

The 3D-Modeller: A Multi-Purpose Vision Platform

Robots acting in populated environments must be capable of safe but also time efficient navigation. Trying to completely avoid regions resulting from worst case predictions of the obstacle dynamics may leave no free space for a robot to move, especially in environments with high dynamic. This work presents an algorithm for a ldquosoftrdquo risk mapping of dynamic objects leaving the complete space free of static objects for path planning. Markov Chains are used to model the dynamics of moving persons and predict their potential future locations. These occlusion estimations are mapped into risk regions which serve to plan a path through potentially obstructed space searching for the trade-off between detour and time delay. The offline computation of the Markov Chain model keeps the computational effort low, making the approach suitable for online applications.

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Probabilistic mapping of dynamic obstacles using Markov chains for replanning in dynamic environments

As robots are gradually leaving highly structured factory environments and moving into human populated environments, they need to possess more complex cognitive abilities. Not only do they have to operate efficiently and safely in natural populated environments, but also be able to achieve higher levels of cooperation and interaction with humans. The autonomous city explorer (ACE) project envisions to create a robot that will autonomously navigate in an unstructured urban environment and find its way through interaction with humans. To achieve this, research results from the fields of autonomous navigation, path planning, environment modeling, and human-robot interaction are combined. In this paper a novel hardware platform is introduced, a system overview is given, the research foci of ACE are highlighted, approaches to the occurring challenges are proposed and analyzed, and finally some first results are presented.

The autonomous city explorer project: aims and system overview

A novel multi-focal four camera vision system is presented. The dynamic performance reaches velocities and accelerations exceeding far beyond human capabilities. Extreme wide-angle aperture angles to narrow foveated are provided simultaneously and are independently controllable. The vision system is designed in the context of the new development of the successor model of the humanoid robot Johnnie targeting at real-world wide distance range applications. This paper presents the design, kinematic and perception models, and mechatronical integration. The dynamic performance is evaluated in experiments. A novel multi-camera view direction stabilization strategy is presented and experimentally validated

M. Bachmayer

Papers

A multi-focal high-performance vision system