Achievement of localization system for humanoid robots with virtual horizontal scan relative to improved odometry fusing internal sensors and visual information

To achieve tasks in unknown environments with high reliability, highly accurate localization during task execution is necessary for humanoid robots. In this paper, we discuss a localization system which can be applied to a humanoid robot when executing tasks in the real world. During such tasks, humanoid robots typically do not possess a referential to a constant horizontal plane which can in turn be used as part of fast and cost efficient localization methods. We solve this problem by first computing an improved odometry estimate through fusing visual odometry, feedforward commands from gait generator and orientation from inertia sensors. This estimate is used to generate a 3D point cloud from the accumulation of successive laser scans and such point cloud is then properly sliced to create a constant height horizontal virtual scan. Finally, this slice is used as an observation base and fed to a 2D SLAM method. The fusion process uses a velocity error model to achieve greater accuracy, which parameters are measured on the real robot. We evaluate our localization system in a real world task execution experiment using the JAXON robot and show how our system can be used as a practical solution for humanoid robots localization during complex tasks execution processes.