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Outdoor, unstructured and cross-country environments introduce several challenging problems such as highly complex scene geometry, ground cover variation, uncontrolled lighting, weather conditions and shadows for vision-based terrain classification of Unmanned Ground Vehicles (UGVs). Color stereo vision is mostly used for UGVs, but the present stereo vision technologies and processing algorithms are...
This paper describes the sea trial experiments conducted to validate the physical implementation of online EKF-SLAM onboard the Meredith Autonomous Underwater Vehicle (AUV) in shallow coastal waters. This paper provides details of the hardware and software implementations of the EKF-SLAM algorithms running real-time on the AUV to perform feature-based navigation. The computation resources onboard...
This paper presents an alternative formulation for the Bayesian feature-based simultaneous localisation and mapping (SLAM) problem, using a random finite set approach. For a feature based map, SLAM requires the joint estimation of the vehicle location and the map. The map itself involves the joint estimation of both the number of features and their states (typically in a 2D Euclidean space), as an...
With few exceptions, most of the existing noise reduction and data segmentation algorithms are only suited to image data. Therefore, an adaptive smoothing algorithm, with model-based masks, within a scale space framework is proposed for range data in this paper. This algorithm smoothes range data that conform to predefined, geometric models, while leaving other data points unaffected. The convergence...
A Rao-Blackwellized particle filter approach is an effective means to estimate the full SLAM posterior. The approach provides for the use of raw sensor measurements directly in SLAM, thus obviating the need to extract landmarks using complex feature extraction methods and data association. In this paper a solution framework based on Rao-Blackwellized particle filters (RB) and genetic algorithms (GA)...
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