Abstract
We present an implementation of a novel foveating 3D sensor concept, inspired by the human eye, which intends to allow future robots to better interact with their surroundings. The sensor is based on a time-of-flight laser scanning technology, where each range distance measurement is performed individually for increased quality. Micro-mirrors enable detailed control on where and when each sample point is acquired in the scene. By finding regions-of-interest (ROIs) and mainly concentrating the data acquisition here, the spatial resolution or frame rate of these ROIs can be significantly increased compared to a non-foveating system. Foveation is enabled through a real-time implementation of a feed-back control loop for the sensor hardware, based on vision algorithms for 3D scene analysis. In this paper, we describe and apply an algorithm for detecting ROIs based on motion detection in range data using background modeling. Heuristics are incorporated to cope with camera motion. We report first results applying this algorithm to scenes with moving objects, and show that the foveation capability allows the frame rate to be increased by up to 8.2 compared to a non-foveating sensor, utilizing up to 99% of the potential frame rate increase. The incorporated heuristics significantly improves the foveation’s performance for moving camera scenes.
