Probabilistic volumetric mapping (PVM) represents a 3D environmental map for an autonomous robotic navigational task. A popular implementation such as Octomap is widely used in the robotics community for such a purpose. The Octomap relies on octree to represent a PVM and its main bottleneck lies in massive ray-shooting to determine the occupancy of the underlying volumetric voxel grids. In this paper, we propose GPU-based ray shooting to drastically improve the ray shooting performance in Octomap. Our main idea is based on the use of recent ray-tracing RTX GPU, mainly designed for real-time photo-realistic computer graphics and the accompanying graphics API, known as DXR. Our ray-shooting first maps leaf-level voxels in the given octree to a set of axis-aligned bounding boxes (AABBs) and employ massively parallel ray shooting on them using GPUs to find free and occupied voxels. These are fed back into CPU to update the voxel occupancy and restructure the octree. In our experiments, we have observed more than three-orders-of-magnitude performance improvement in terms of ray shooting using ray-tracing RTX GPU over a state-of-the-art Octomap CPU implementation, where the benchmarking environments consist of more than 77K points and 25K~34K voxel grids.

中文翻译：

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使用Ray-Tracing图形硬件加速概率体积映射

概率体积映射（PVM）表示自主机器人导航任务的3D环境图。为此目的，在机器人技术界广泛使用了诸如Octomap之类的流行实现。Octomap依靠八叉树来表示PVM，其主要瓶颈在于进行大量射线拍摄以确定底层体积体素网格的占用率。在本文中，我们提出了基于GPU的射线拍摄以大大提高Octomap的射线拍摄性能。我们的主要思想基于最近使用的光线追踪RTX GPU（主要用于实时照片级逼真的计算机图形和随附的图形API，即DXR）的使用。我们的射线拍摄首先将给定八叉树中的叶级体素映射到一组轴对齐的边界框（AABB），然后使用GPU在其上进行大规模平行射线拍摄，以找到自由和占用的体素。这些反馈到CPU中以更新体素占用率并重组八叉树。在我们的实验中，我们观察到在最先进的Octomap CPU实现上使用光线追踪RTX GPU进行光线拍摄时，性能提高了三个数量级以上，其中基准测试环境包括77K点和25K〜34K体素网格。