Robots’ State Estimation and Observability Analysis Based on Statistical Motion Models

This article presents a generic motion model to capture mobile robots’ dynamic behaviors (translation and rotation). The is based on statistical models driven by white random processes formulated into full state estimation algorithm the error-state extended Kalman filtering framework (ESEKF). major benefits of this method are its versatility, being applicable different robotic systems without accurately modeling specific dynamics, ability estimate robot’s (angular) acceleration, jerk, or higher order states with low delay. Mathematical analyses numerical simulations presented show properties model-based reveal connection existing low-pass filters. Furthermore, new paradigm developed for observability analysis developing Lie derivatives associated partial differentiation directly manifolds. It shown that much simpler more natural than methods quaternion parameterizations. also scalable high-dimensional systems. A novel thin set concept introduced characterize unobservable subset system states, providing theoretical foundation operating manifolds in high dimension. Finally, extensive experiments, including extrinsic calibration (both POS-IMU IMU-IMU) quadrotor unmanned aerial vehicle (UAV), handheld platform, ground vehicle, conducted. Comparisons proposed can effectively all parameters, translation/angular other variables (e.g., position, velocity, attitude) accuracy