Robotics Institute
Carnegie Mellon University
Abstract:
In this thesis I explore the topic of geolocation from range. A robust method for localization and SLAM (Simultaneous Localization and Mapping) is proposed. This method uses a polar parameterization of the state to achieve accurate estimates of the nonlinear and multi-modal distributions in range-only systems. Several experimental evaluations on real robots reveal the reliability of this method.
Scaling such a system to large network of nodes, increases the computational load on the system due to the increased state vector. To alleviate this problem, we propose the use of a distributed estimation algorithm based on the belief propagation framework. This method distributes the estimation task, such that each node only estimates its local network, greatly reducing the computation performed by any individual node. However, the method does not provide any guarantees on the convergence of its solution in general graphs. Convergence is only guaranteed for non-cyclic graphs (ie. trees). Thus, I propose to formulate an extension to this approach that provides guarantees on its convergence and an improved approximation of the true graph inference problem.
Scaling in the traditional sense involves extensions to deal with growth in the size of the operating environment. In large, feature-less environments, maintaining a globally consistent estimate of a group of mobile agents is difficult. In this thesis, I propose the use of a multi-robot coordination strategy to achieve the tight coordination necessary to obtain an accurate global estimate. The proposed approach will be demonstrated using both simulation and experimental testing with real robots.
No comments:
Post a Comment