Earlier work in the field of intent recognition used laser and cameras to track people and extract physical information to train and test models of intention. With the progression of computation abilities, Neural Networks have made it possible to extract additional information that earlier work was not able to take advantage of, primarily person pose information. With this new information, intent recognition systems should be able to differentiate in finer detail between neighboring intents. We combine all the available information for both pose and movement descriptors to generate profiles about each person in the scene. This new pose information also makes it possible to track people in laser data more reliably. To do this tracking well, calibration between the sensors in critical, so we propose an iterative method for calibration that can produce a result not just for robots with sensors aligned toward the face of the target but also for sensor arrays that may observe the target from disparate directions. The calibration method we developed is only one of just a few current methods that can solve the calibration of a laser range finder and an RGB camera with only a single sample and unknown target dimensions.

CHS: Small: Socially-Aware Navigation, National Science Foundation PI: David Feil-Seifer, co-PI: Monica Nicolescu, Amount: $500,000, Sept. 1, 2017 - May 31, 2022