Robust Real-Time 3D Person Detection for Indoor and Outdoor Applications

Abstract

Fast and robust person detection is one of the most important tasks for robotic applications involving human interaction. Particularly in mobile robotics this task is still challenging. Though there are already reliable and real-time capable approaches, they are usually computationally expensive. They either require GPUs or multiple CPU cores in order to work properly. Furthermore, some of the approaches are designed for special environments and sensor types, which reduces general applicability. In this work, we present a robust, generic and lightweight solution for real-time 3D person detection. Since our approach requires only a single CPU thread, it can be run as a background process and is suitable for smaller robotic systems. We demonstrate applicability to indoor and outdoor scenarios using different 3D sensor types separately. Moreover, we are able to show that the proposed method outperforms other state-of-the-art approaches, including a DCNN.