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Human-in-the loop robotic systems have the potential to handle complex tasks in unstructured environments, by combining the cognitive skills of a human operator with autonomous tools and behaviors. Along these lines, we present a system for remote human-in-the-loop grasp execution. An operator uses a computer interface to visualize a physical robot and its surroundings, and a point-and-click mouse interface to command the robot. We implemented and analyzed four different strategies for performing grasping tasks, ranging from direct, real-time operator control of the endeffector pose, to autonomous motion and grasp planning that is simply adjusted or confirmed by the operator. Our controlled experiment (N =48) results indicate that people were able to successfully grasp more objects and caused fewer unwanted collisions when using the strategies with more autonomous assistance. We used an untethered robot over wireless communications, making our strategies applicable for remote, human-in-the-loop robotic applications.

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SURF (Speeded Up Robust Features) is a detector and descriptor of local scale-and rotation-invariant image features. By using integral images for image convolutions it is faster to compute than other state-of-the-art algorithms, yet produces comparable or even better results by means of repeatability, distinctiveness and robustness. A library implementing SURF is provided by the authors. However, it is closedsource and thus not suited as a basis for further research.Several open source implementations of the algorithm exist, yet it is unclear how well they realize the original algorithm. We have evaluated different SURF implementations written in C++ and compared the results to the original implementation.We have found that some implementations produce up to 33% lower repeatability and up to 44% lower maximum recall than the original implementation, while the implementation provided with the software Pan-o-matic produced almost identical results.We have extended the Pan-o-matic implementation to use multithreading, resulting in an up to 5.1 times faster computation on an 8-core machine. We describe our comparison criteria and our ideas that lead to the speed-up. Our software is put into the public domain.