Laser reflection intensity and multi-layered laser range finders for people detection

Successful detection of people is a basic requirement for a robot to achieve symbiosis in people's daily life. Specifically, a mobile robot designed to follow people needs to keep track of people's position through time, for it defines the robot's position and trajectory. In this work we introduce the usage of reflection intensity data of Laser Range Finders (LRF) arranged in multiple layers for people detection. We use supervised learning to train strong classifiers including intensity-based features. Concretely, we propose a calibration method for laser intensity and introduce new intensity-based features for people detection which are combined with range-based features in a strong classifier using supervised learning. We provide experimental results to evaluate the effectiveness of these features. This work is an step towards of our main research project of developing a social autonomous mobile robot acting as member of a people group.