Abstract. Pseudolites, ground-based GPS signal transmitters, can significantly enhance the GPS satellite geometry or can even be an independent positioning system. However, as pseudolites are very close to the receivers, error effects are different from the traditional GPS and should be considered and modeled in a different way. Tropospheric delay is one of the largest error sources in pseudolite positioning, as pseudolite signal propagates through the lower troposphere which is very difficult to be modeled due to spatial variations in atmosphere. The objective of this research is to analyse pseudolite tropospheric delay modelling methods and to select the optimal tropospheric delay models for different applications.Several methods to estimate the tropospheric delay for pseudolite positioning are introduced and compared. One approach is to utilize single-differenced GPS tropospheric models. Another one is to compute the tropospheric delay as a function of the local refractivity along the pseudolite signal path. The ratio method used for Electronic Distance Measurement (EDM) can also be applied to estimate tropospheric delay.Experiments with simulation and real flight test data are conducted in this study to investigate the proposed methods. The advantages and limitations of each method are analysed. The mode defined by RTCA and its modification are suitable for a low elevation and short range application, such as LAAS and local ground based applications. Models derived from single-differenced NMF and Saastamoinen models perform well in long range and high elevation but have a big bias in low elevation. And the model derived from the Hopfield model performs relatively well in all the range and elevation.
Numerous path-planning studies have been conducted in past decades due to the challenges of obtaining optimal solutions. This paper reviews multi-robot path-planning approaches and decision-making strategies and presents the path-planning algorithms for various types of robots, including aerial, ground, and underwater robots. The multi-robot path-planning approaches have been classified as classical approaches, heuristic algorithms, bio-inspired techniques, and artificial intelligence approaches. Bio-inspired techniques are the most employed approaches, and artificial intelligence approaches have gained more attention recently. The decision-making strategies mainly consist of centralized and decentralized approaches. The trend of the decision-making system is to move towards a decentralized planner. Finally, the new challenge in multi-robot path planning is proposed as fault tolerance, which is important for real-time operations.
This paper first demonstrates an interesting property of bundle adjustment (BA), "scale drift correction" property. Here "scale drift correction" means that BA can converge to the correct solution (up to a scale) even if the initial values of the camera pose translations and point feature positions are calculated using different scale factors. This property together with other properties of BA makes BA the best approach for monocular SLAM when no camera motion information is available, although the computational cost of BA is an issue. This naturally leads to the idea of using local BA and map joining to solve large-scale monocular SLAM problem, which is proposed in this paper. The local maps are built through Scale-Invariant Transform Feature (SIFT) detector and matching, random sample consensus paradigm (RANSAC) at different levels for robust outlier removal, and BA for optimization. To reduce the computational cost of the large-scale map building, the features in each local map are properly selected and then the local maps are combined using a recently developed 3D map joining algorithm. The proposed large-scale monocular SLAM algorithm is evaluated using a publicly available dataset. It is shown that the camera poses estimate is very accurate as compared with the ground truth provided.
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