3 accepted papers
This paper develops a probabilistic simultaneous estimation and modeling (SEAM) framework for estimating a robot’s state and correcting its motion model parameters. This is done by incorporating model uncertainty in state prediction and correcting parameters via optimization. In the proposed techniq…
This paper presents a technique for state estimation of a multi-link system having no joint encoders, which can only be partially observed by a camera. To fully observe the system without changing the current configuration, a gyroscope and an accelerometer are attached to each link as dead-reckoning…
This paper proposes a probabilistic framework for classification of evolving targets, leveraging the principles of recursive Bayesian estimation in a perception-oriented context. By implementing a Gaussian toroid prediction model of the perception target's evolution, the proposed recursive Bayesian…