The ability to generate accurate and detailed three‐dimensional (3D) maps of a scene from a mobile platform is an essential technology for a wide variety of applications from robotic navigation to geological surveying. In many instances, the best vantage point is from above, and as a result, there is a growing demand for low‐altitude mapping solutions from micro aerial vehicles such as small quadcopters. Existing lidar‐based 3D airborne mapping solutions rely on GPS/INS solutions for positioning, or focus on producing relatively low‐fidelity or locally focused maps for the purposes of autonomous navigation. We have developed a general‐purpose airborne 3D mapping system capable of continuously scanning the environment during flight to produce accurate and dense point clouds without the need for a separate positioning system. A key feature of the system is a novel passively driven mechanism to rotate a lightweight 2D laser scanner using the rotor downdraft from a quadcopter. The data generated from the spinning laser is input into a continuous‐time simultaneous localization and mapping (SLAM) solution to produce an accurate 6 degree‐of‐freedom trajectory estimate and a 3D point cloud map. Extensive results are presented illustrating the versatility of the platform in a variety of environments including forests, caves, mines, heritage sites, and industrial facilities. Comparison with conventional surveying methods and equipment demonstrates the high accuracy and precision of the proposed solution.

major goal of humanoid robotics is to enable safe and reliable human-robot collaboration in realworld scenarios. In this article, we present ARMAR-6, a new high-performance humanoid robot for various tasks, including but not limited to grasping, mobile manipulation, integrated perception, bimanual collaboration, compliant-motion execution, and natural language understanding. We describe how the requirements arising from these tasks influenced our major design decisions, resulting in vertical integration during the joint hardware and software development phases. In particular, the entire hardware-including its structure, sensor-actuator units, and low-level controllers-as well as its perception, grasping and manipulation skills, task coordination, and the entire software architecture were all developed by one team of engineers. Component interaction is facilitated by our software framework ArmarX, which

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Active tactile perception is a powerful mechanism to collect contact information by touching an unknown object with a robot finger in order to enable further interaction with the object or grasping of the object. The acquired object knowledge can be used to build object shape models based on such usually sparse tactile contact information. In this paper, we address the problem of object shape reconstruction from sparse tactile data gained from a robot finger that yields contact information and surface orientation at the contact points. To this end, we present an exploration algorithm which determines the next best touch target in order to maximize the estimated information gain and to minimize the expected costs of exploration actions. We introduce the Information Gain Estimation Function (IGEF), which combines different goals as measure for the quantification of the cost-aware information gain during exploration. The IGEF-based exploration strategy is validated in simulation using 48 publicly available object models and compared to state-of-the-art Gaussian processes-based exploration approaches. The results show the performance of the approach regarding exploration efficiency, cost-awareness and suitability for application in real tactile sensing scenarios.

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