I see you lying on the ground — Can I help you? Fast fallen person detection in 3D with a mobile robot

Abstract

One important function in assistive robotics for home applications is the detection of emergency cases, like falls. In this paper, we present a new detection system which can run on a mobile robot to detect persons after a fall event robustly. The system is based on 3D Normal Distributions Transform (NDT) maps on which a powerful segmentation is applied. Segments most likely belonging to a person lying on the ground are grouped into clusters. After extracting features with a soft encoding approach, each cluster is classified separately. Our experiments show that the system is able to reliably detect fallen persons in real-time. It clearly outperforms other 3D state-of-the-art approaches. We can show that our system is able to handle even very challenging situations, where fallen persons are very close to other objects in the apartment. Such complex fall events often occur in real-world applications.