Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment
2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)(2019)
摘要
This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real-world environments, such as urban streets or crowded areas, the efficient and robust incorporation of temporal dependencies into otherwise static occupancy models remains a challenge. We propose a method to capture the spatial uncertainty of moving objects and incorporate this uncertainty information into a continuous occupancy map represented in a rich high-dimensional feature space. Experiments performed using LIDAR data verified the real-time performance of the algorithm.
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关键词
real-time occupancy predictions,static occupancy models,continuous occupancy map,high-dimensional feature space,data-efficient model,crowded unstructured outdoor environments,dynamic Hilbert maps,temporal dependencies,3D laser data,2D laser data
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