On-board odometry estimation for 3D vision-based SLAM of humanoid robot

Abstract

This paper addresses a vision-based 3D motion estimation framework for humanoid robots, which copes with human-like walking pattern. A humanoid robot, called Roboray, is designed for dynamic walking control with heel-toe motion like a human. In spite of stability and energy efficiency of the dynamic walking, it accompanies larger swaying motion and more uncertainty in camera movement than the conventional ZMP (Zero Moment Point)-based walking does. The framework effectively uses on-board odometry information from the robot to improve the performance of the visionbased motion estimation. To accomplish this, we propose an onboard odometry filter which fuses kinematic odometry, visual odometry, and raw IMU data. And the odometry filter is combined with vision-based SLAM to provide accurate motion model, so it enhances the SLAM estimates. Experimental results in indoor environment verify that the framework can successfully estimate the pose of Roboray in real-time.