Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ...
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ISBN:
(纸本)9781728136059
Autonomous mobile robots are becoming increasingly important in many industrial and domestic environments. Dealing with unforeseen situations is a difficult problem that must be tackled in order to move closer to the ultimate goal of life-long autonomy. In computervision-based methods employed on mobile robots, such as localization or navigation, one of the major issues is the dynamics of the scenes. The autonomous operation of the robot may become unreliable if the changes that are common in dynamic environments are not detected and managed. Moving chairs, opening and closing doors or windows, replacing objects on the desks and other changes make many conventional methods fail. To deal with that, we present a novel method for change detection based on the similarity of local visual features. The core idea of the algorithm is to distinguish important stable regions of the scene from the regions that are changing. To evaluate the change detection algorithm, we have designed a simple visual localization framework based on feature matching and we have performed a series of real-world localization experiments. The results have shown that the change detection method substantially improves the accuracy of the robot localization, compared to using the baseline localization method without change detection.
Artificial vision in robotics involves real time detection of objects for fast decision making. Such intelligent systems require efficient algorithms and big learning database of examples for producing robust classifi...
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Artificial vision in robotics involves real time detection of objects for fast decision making. Such intelligent systems require efficient algorithms and big learning database of examples for producing robust classifiers. Several methods of objects detection and tracking have been proposed in the literature. However, even though the detection rates have been improved, the processing time and the complexity of the models still representing a key challenge. In this paper, we present a real time object detection and tracking framework based on Adaboost classification, where a strong classifier is generated using an iterative combination of weak learners. This method is based on the use of discriminative features by analyzing different regions of the input image. Instead of performing a full traversal in the entire search space of all possible visual features, we propose to use intelligent heuristics for accelerating time processing and extracting relevant features in the image that lead to a best detection rate. The meta-heuristics involve the use of genetic algorithms, particle swarm optimization, random walk and a novel hybrid combination of these methods. The obtained results, in a case of intelligent transportation system, have shown considerable improvements in term of computation time, efficiency and accuracy.
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