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A novel approach to estimate localizability for mobile robots is presented based on probabilistic grid map (PGM). Firstly, a static localizability matrix is proposed for off-line estimation over the priori PGM. Then a dynamic localizability matrix is proposed to deal with unexpected dynamic changes. These matrices describe both localizability index and localizability direction quantitatively. The...
This paper deals with distributed information processing in sensor networks. We propose the Hypothesizing Distributed Kalman Filter that incorporates an assumption of the global measurement model into the distributed estimation process. The procedure is based on the Distributed Kalman Filter and inherits its optimality when the assumption about the global measurement uncertainty is met. Recursive...
The work demonstrates how the methods of Active Sensing (AS), based on the theory of optimal experimental design, can be applied for a location estimation scenario. The simulated problem consists of several mobile and fixed nodes where each mobile unit is equipped with a gyroscope and an incremental path encoder and is capable to make a selective range measurement to one of several fixed anchors as...
This paper presents an in-depth evaluation of filter algorithms utilized in the estimation of 3D position and attitude for UAV using stereo vision based Visual SLAM integrated with feature detection and matching techniques i.e., SIFT and SURF. The evaluation's aim was to investigate the accuracy and robustness of the filters' estimation for vision based navigation problems. The investigation covered...
In this paper, we consider the problem of fusing measurements which contain correlated noises within posegraph-based formulations of filtering and estimation problems. We develop a formulation of the Weighted Geometric Density (WGD) fusion algorithm, a generalisation of Covariance Intersection (CI), for posegraphs. We show that this form can generate covariance consistent estimates. We propose two...
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