OctoMap: an efficient probabilistic 3D mapping framework based on octrees
OctoMap: an efficient probabilistic 3D mapping framework based on octrees
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DOI：
10.1007/s10514-012-9321-0
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发表时间：
2013-04-01
影响因子：
3.5
通讯作者：
Burgard, Wolfram
中科院分区：
文献类型：
--
作者：
Hornung, Armin;Wurm, Kai M.;Burgard, Wolfram
三维模型提供了空间的体积表示，这对于各种机器人应用，包括飞行机器人和配备有操纵器的机器人是重要的。在本文中，我们提出了一个开源的框架来生成体积三维环境模型。我们的映射方法是基于八叉树和使用概率占用估计。它不仅明确地表示被占用的空间，而且还表示自由和未知的区域。此外，我们提出了一种八叉树映射压缩方法，保持3D模型的紧凑。我们的框架是一个开源的C++库，并已成功地应用于几个机器人项目。我们提出了一系列的实验结果进行了真实的机器人和公开的真实世界的数据集。结果表明，我们的方法是能够有效地更新表示和模型的数据一致，同时保持在最低限度的内存需求。
Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.