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Autonomous robot navigation in out-door scenarios gains increasing importance in various growing application areas. Whereas in non-urban domains such as deserts the problem of successful GPS-based navigation appears to be almost solved, navigation in urban domains particularly in the close vicinity of buildings is still a challenging problem. In such(More)
In this paper, we present an integrated navigation system that allows humanoid robots to autonomously navigate in unknown, cluttered environments. From the data of an onboard consumer-grade depth camera, our system estimates the robot's pose to compensate for drift of odometry and maintains a heightmap representation of the environment. Based on this model,(More)
Customer investigations in the banking industry are carried out in connection with prosecutions, administration of estates or other legal actions. The Investigation & Inquiries department of Credit Suisse has to handle approximately 5000 customer investigations per year. So far, the investigation process was very complex, time consuming and costly: Several(More)
—In this paper, we present an integrated approach for robot localization, obstacle mapping, and path planning in 3D environments based on data of an onboard consumer-level depth camera. We rely on state-of-the-art techniques for environment modeling and localization, which we extend for depth camera data. We thoroughly evaluated our system with a Nao(More)
In this article, we present an efficient approach to obstacle detection for humanoid robots based on monocular images and sparse laser data. We particularly consider collision-free navigation with the Nao humanoid, which is the most popular small-size robot nowadays. Our approach first analyzes the scene around the robot by acquiring data from a laser range(More)
In this paper, we present an approach to obstacle detection for collision-free, efficient humanoid robot navigation based on monocular images and sparse laser range data. To detect arbitrary obstacles in the surroundings of the robot, we analyze 3D data points obtained from a 2D laser range finder installed in the robot's head. Relying only on this laser(More)
In this paper, we present an approach to traversability classification solely based on monocular images and odometry estimates. We iteratively estimate the ground plane by detecting and matching features. Since the features are only sparse in the images and do not lead to dense information about traversability, we present a technique to train(More)