Deep Learning based-State Estimation for Holonomic Mobile Robots Using Intrinsic Sensors

Abstract

State estimation is a fundamental component of the navigation system of autonomous mobile robots. Generally, the robot setup is equipped with intrinsic and extrinsic sensors. The state estimators have relied almost on intrinsic sensors such as wheel encoders and inertial measurement units in textureless and structureless environments. This paper will analyze and propose the learning state estimation frameworks for the dead-reckoning of autonomous holonomic vehicles based only on intrinsic sensors. First, we review and categories the intrinsic-only estimation problem. Second, we describe the problem formulation using learning-based techniques. Next, the learning inertial-only estimation is presented with several strategies using the deep learning technique. The initial experiment results are analyzed and deployed using a holonomic mobile robot in real-world environments.