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.

As mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3D object recognition plays an increasingly important role. One of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. Although there exists a plethora of different approaches for estimating normal vectors from 3D point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. This paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. The study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the dasiarightpsila algorithm for the robotics practitioner. The robustness of the methods with respect to noise and neighborhood size is analyzed. All algorithms are benchmarked with simulated as well as real 3D laser data obtained from a mobile robot.

In mobile robotics, the segmentation of range data is an important prerequisite to object recognition and environment understanding. This paper presents an algorithm for realtime segmentation of a continuous stream of incoming range data. The method is an extension of the previously developed RBNN algorithm and proceeds in two phases: Firstly, the normal vector of each incoming point is estimated from its neighborhood, which is continuously monitored. Secondly, new points are clustered according to their Euclidean and angular distance to previously clustered points. An outline of the algorithm complexity as well as the parameters that influence the segmentation performance is provided. Three benchmark scenarios in which the algorithm is deployed on a mobile robot with a laser range finder confirm that the method can robustly segment incoming data at high rates.

In this paper we present a novel method for the efficient segmentation of 3D laser range data. The proposed algorithm is based on a radially bounded nearest neighbor strategy and requires only two parameters. It yields deterministic, repeatable results and does not depend on any initialization procedure. The efficiency of the method is verified with synthetic and real 3D data.

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.