Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring artificial intelligence in the new millennium
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
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Mapping is a fundamental topic for robotics in general and in particular for rescue robotics where the provision of information about the location of victims is a core task. Occupancy grids are the standard way of generating and representing maps, i.e., in form of raster data. But vector representations are for many reasons, especially due to their compactness and the possibility to use very efficient computational geometry algorithms, highly desirable for many applications. Here a novel method for vectorization is presented that is intended to work particularly well with maps. It is based on an evolutionary algorithm that generates vector code for a so to say drawing program. The output of the evolving vector code is compared to the input grid map via a special similarity function as fitness. Experiments are presented that indicate that the approach is indeed a successful method to extract vector data out of grid maps.