We present a benchmarking study of vision-based robotic grasping algorithms with distinct approaches, and provide a comparative analysis. In particular, we compare two machine-learning-based and two analytical algorithms using an existing benchmarking protocol from the literature and determine the algorithm's strengths and weaknesses under different experimental conditions. These conditions include variations in lighting, background textures, cameras with different noise levels, and grippers. We also run analogous experiments in simulations and with real robots and present the discrepancies. Some experiments are also run in two different laboratories using same protocols to further analyze the repeatability of our results. We believe that this study, comprising 5040 experiments, provides important insights into the role and challenges of systematic experimentation in robotic manipulation, and guides the development of new algorithms by considering the factors that could impact the performance. The experiment recordings and our benchmarking software are publicly available.

Intel is back with another RealSense camera, but this one has a slight twist: it's meant to give machines a sense of place. The lengthily-titled RealSense Tracking Camera T265 uses inside-out tracking (that is, it doesn't need outside sensors) to help localize robots and other autonomous machines, particularly in situations where GPS is unreliable or non-existent. A farming robot, for instance, could both map a field as well as adapt on the fly to obstacles like buildings and rocks. It's relying on the same Myriad 2 processing hardware seen in other recent projects, which takes much of the processing burden away from other devices without heavy energy demands. The only requirements are 1.5W of power, a USB connection and enough memory to power it up. You could stick this on a drone, to put it another way.

Machine vision enables robots and unmanned systems to more easily move through and interact with their environments. Cameras are the robots' eyes, while the central processing unit is the brain that interprets all the captured data. Intel RealSense is one technology helping machines see more like humans. Machine vision is nothing new, of course, but 3D depth sensing in real time has long been a major challenge for the robotics industry. While laser, infrared, and radar offer accurate depth sensing, these technologies would drive the price of autonomous systems through the roof.