LaserVAE for Feature Description and an Application of Global Self-localization

Abstract

For accurate global self-localization, researches for the feature description of the laser-scan data have been actively conducted. Main approaches to the feature description are to design feature descriptor based on human knowledge regarding the specific environment, e.g., office and hallway. However, in real robot navigation tasks such as a security patrol robot, the robot would be applied to a variety of environments and it is expensive if the users need to tune the design at every environment. To alleviate such problem, we propose to extend the state-of-the-art variational auto-encoder (VAE) by introducing the step-edge detector, which detects non-continuous transition emerged frequently at the laser scan data due to the limitation of distance measurement. With our proposed method, called “LaserVAE”, the feature descriptor of the laser scan is automatically tuned given unknown environments. Through experiments with a real self-localization with a 2D laser scanner, we demonstrate the effectiveness of the proposed method.