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Marius Muja from University of British Columbia returned to Willow Garage this summer to continue his work object recognition. In addition to working on an object detector that can scale to a large number of objects, he has also been designing a general object recognition infrastructure. One problem that many object detectors have is that they get slower as they learn new objects. Ideally we want a robot that goes into an environment and is capable of collecting data and learning new objects by itself. In doing this, however, we don't want the robot to get progressively slower as it learns new objects. Marius worked on an object detector called Binarized Gradient Grid Pyramid (BiGGPy), which uses the gradient information from an image to match it to a set of learned object templates. The templates are organized into a template pyramid. This tree structure has low resolution templates at the root and higher resolution templates at each lower level. During detection, only a fraction of this tree must be explored. This results in big speedups and allows the detector to scale to a large number of objects.

On Monday, National Instruments announced one such platform. It's called LabView Robotics. In addition to LabView, the popular data-acquisition application, the package includes a bunch of tools specific to robotics. It can import codes in various formats (C, C++, Matlab, VHDL), offers a library of drivers for a wide variety of sensors and actuators, and has modules for implementation of real-time and embedded hardware. NI says engineers could use the package to both design and run their robotic systems.