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U rban search and rescue missions raise special requirements on robotic systems. Small aerial systems provide essential support to human task forces in situation assessment and surveillance. As external infrastructure for navigation and communication is usually not available, robotic systems must be able to operate autonomously. A limited payload of small aerial systems poses a great challenge to the system design. The optimal tradeoff between flight performance, sensors, and computing resources has to be found. Communication to external computers cannot be guaranteed; therefore, all

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Visual force feedback resulted in reduced suture breakage, lower forces, and decreased force inconsistencies among novice robotic surgeons, although elapsed time and knot quality were unaffected. In contrast, visual force feedback did not affect these metrics among surgeons experienced with the da Vinci system. These results suggest that visual force feedback primarily benefits novice robot-assisted surgeons, with diminishing benefits among experienced surgeons.

In this paper, we present an efficient algorithm for 3D object recognition in presence of clutter and occlusions in noisy, sparse and unsegmented range data. The method uses a robust geometric descriptor, a hashing technique and an efficient RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method recognizes multiple model instances and estimates their position and orientation in the scene. The algorithm scales well with the number of models and its main procedure runs in linear time in the number of scene points. Moreover, the approach is conceptually simple and easy to implement. Tests on a variety of real data sets show that the proposed method performs well on noisy and cluttered scenes in which only small parts of the objects are visible.

We present daVinci Canvas: a telerobotic surgical system with integrated robot-assisted laparoscopic ultrasound capability. DaVinci Canvas consists of the integration of a rigid laparoscopic ultrasound probe with the daVinci robot, video tracking of ultrasound probe motions, endoscope and ultrasound calibration and registration, autonomous robot motions, and the display of registered 2D and 3D ultrasound images. Although we used laparoscopic liver cancer surgery as a focusing application, our broader aim was the development of a versatile system that would be useful for many procedures.

Abstract-In this paper, we present an efficient 3D object recognition and pose estimation approach for grasping procedures in cluttered and occluded environments. In contrast to common appearance-based approaches, we rely solely on 3D geometry information. Our method is based on a robust geometric descriptor, a hashing technique and an efficient, localized RANSAC-like sampling strategy. We assume that each object is represented by a model consisting of a set of points with corresponding surface normals. Our method simultaneously recognizes multiple model instances and estimates their pose in the scene. A variety of tests shows that the proposed method performs well on noisy, cluttered and unsegmented range scans in which only small parts of the objects are visible. The main procedure of the algorithm has a linear time complexity resulting in a high recognition speed which allows a direct integration of the method into a continuous manipulation task. The experimental validation with a 7-degrees-of-freedom Cartesian impedance controlled robot shows how the method can be used for grasping objects from a complex random stack. This application demonstrates how the integration of computer vision and softrobotics leads to a robotic system capable of acting in unstructured and occluded environments.

Abstract-Accurate maps of the static environment are essential for many advanced driver-assistance systems. In this paper a new method for the fast computation of occupancy grid maps with laser range-finders and radar sensors is proposed. The approach utilizes the Graphics Processing Unit to overcome the limitations of classical occupancy grid computation in automotive environments. It is possible to generate highly accurate grid maps in just a few milliseconds without the loss of sensor precision. Moreover, in the case of a lower resolution radar sensor it is shown that it is suitable to apply super-resolution algorithms to achieve the accuracy of a higher resolution laserscanner. Finally, a novel histogram based approach for road boundary detection with lidar and radar sensors is presented.