Robust and Autonomous Stereo Visual-Inertial Navigation for Non-Holonomic Mobile Robots

Abstract

Unlike micro aerial vehicles, most mobile robots have non-holonomic constraints, which makes lateral movement impossible. Consequently, the vision-based navigation systems that perform accurate visual feature initialization by moving the camera to the side to ensure a sufficient parallax of the image are degraded when applied to mobile robots. Generally, to overcome this difficulty, a motion model based on wheel encoders mounted on a mobile robot is used to predict the pose of a robot, but it is difficult to cope with errors caused by wheel slip or inaccurate wheel calibration. In this study, we propose a robust autonomous navigation system that uses only a stereo inertial sensor and does not rely on wheel-based dead reckoning. The observation model of the line feature modified with vanishing-points is applied to the visual-inertial odometry along with the point features so that a mobile robot can perform robust pose estimation during autonomous navigation. The proposed algorithm, i.e., keyframe-based autonomous visual-inertial navigation (KAVIN) supports the entire navigation system and can run onboard without an additional graphics processing unit. A series of experiments in a real environment indicated that the KAVIN system provides robust pose estimation without wheel encoders and prevents the accumulation of drift error during autonomous driving.