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This paper presents the development of a state observer for a 2-axle railway vehicle with solid axle wheelsets. A plan view model of the vehicle is presented and a Kalman filter is developed to estimate 18 states from 8 inertial measurements. The required measurements are the lateral acceleration and yaw velocity of the vehicle body and the same measurements plus the roll velocity for the two wheelsets,...
The vehicle's localization is classically achieved by Bayesian methods like Extended Kalman Filtering. Such methods provide an estimated position with its associated uncertainty. Bounded-error approaches (Bounded-Error State Estimation and Constraints Propagation) use interval analysis and work in a different way as they provide a possible set of positions. An advantage of bounded-error approaches...
This paper presents a map-aided GPS/INS localization system developed for a map-based driver safety assistance system. A low-order constrained Unscented Kalman Filter (CUKF) is adopted to fuse the GPS, INS and high-accuracy map data to provide robust lane-level vehicle position and heading estimates for time-critical safety applications. The road data from digital map is formulated as state constraints...
Exploration always occurs in the presence of uncertainty. In this paper, we consider path planning for autonomous vehicles equipped with range-based sensors and traveling in an uncertain area. The mission of the vehicles is to explore a set of objects of interest while reducing uncertainty in object position, visibility and state. A connection is shown between the Kalman filter (used to reduce uncertainty)...
Abstract-This paper introduces a kinematic model of a deep-sea mining vehicle in presence of sliding parameters. The model describes both the noises features of sliding parameters and the deep-sea condition features. To handle sliding parameters noises, a recursive algorithm to minimize difference between the filter-computed and the actual innovation covariance is adopted, which is a novel integrated...
This paper describes an improved solution to the simultaneous localization and mapping (SLAM) problem based on pseudolinear models. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. A state estimator which can be designed to use the nonlinearity as it is...
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