Detecting and tracking humans using a man-portable robot

Abstract

Large gains in the automation of human detection and tracking techniques have been made over the past several years. Several of these techniques have been implemented on larger robotic platforms, in order to increase the situational awareness provided by the platform. Further integration onto a smaller robotic platform that already has obstacle detection and avoidance capabilities would allow these algorithms to be utilized in scenarios that are not plausible for larger platforms, such as entering a building and surveying a room for human occupation with limited operator intervention. However, transitioning these algorithms to a man-portable robot imparts several unique constraints, including limited power availability, size and weight restrictions, and limited processor ability. Many imaging sensors, processing hardware, and algorithms fail to adequately address one or more of these constraints. In this paper, we describe the design of a payload suitable for our chosen man-portable robot, the iRobot Packbot. While the described payload was built for a Packbot, it was carefully designed in order to be platform agnostic, so that it can be used on any man-portable robot. Implementations of several existing motion and face detection algorithms that have been chosen for testing on this payload are also discussed in some detail.