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In autonomous navigation systems, localization is the most important problem to be solved. In this note, we propose the algorithm to localize an unmanned ground vehicle (UGV) equipped with a global positioning system (GPS), an odometer and an electronic compass. The proposed scheme is based on the unscented Kalman filter (UKF). To improve the accuracy and robustness, the novel noise model of data...
The main contribution of this paper is an analysis of the FastSLAM algorithm for simultaneous localization and mapping (SLAM) problem. The convergence properties of the landmark uncertainty for FastSLAM are provided. The proofs clearly show that the limit of the uncertainty for the landmark estimation has no relationship with the vehicle's initial pose uncertainty. Furthermore, the consistency of...
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...
Path planning algorithms that incorporate risk and uncertainty need to be able to predict the evolution of path-following error statistics for each candidate plan. We present an analytic method to predict the evolving error statistics of a holonomic vehicle following a reference trajectory in a planar environment. This method is faster than integrating the plant through time or performing a Monte...
Pose estimation is one of the most important part of guidance systems in unmanned vehicles. In this paper, we consider the problem of pose estimation of unmanned ground vehicle (UGV) equipped with a global positioning system (GPS), an odometer and an electronic compass. The unscented Kalman filter (UKF) is used to fuse the information from those sensors. To improve the accuracy and robustness, we...
Terrain aided navigation techniques are attractive for obtaining submerged position fixes for an underwater vehicle. However, their use is dependent on a certain degree of variation in the terrain. This paper investigates the performance of terrain navigation algorithms in an area with pockmarks, i.e. craters in the sea bed formed by expulsion of gas or water from the sediments. We use real terrain...
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...
In many mobile robot application, navigation is a critical issues. This paper deals with the localization and building problem in an unknown indoor environment. We propose an exploration based on the use of the sensorial data provided by one CCD camera and odometer system. In this paper, the first part of our study is linked to the problem of Vehicle Model and the algorithm model. The second part...
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