论文标题
使用射线追踪图形硬件加速概率的体积映射
Accelerating Probabilistic Volumetric Mapping using Ray-Tracing Graphics Hardware
论文作者
论文摘要
概率体积映射（PVM）代表了自动机器人导航任务的3D环境图。诸如OCTOMAP之类的流行实现在机器人社区中被广泛用于此目的。 OCTOMAP依靠OCTREE代表PVM，其主瓶颈在于巨大的射线射击，以确定基础体积素的占用率。在本文中，我们提出了基于GPU的射线射击，以极大地改善Octomap的射线射击性能。我们的主要思想是基于最近射线追踪RTX GPU的使用，该射线追踪GPU主要用于实时照片现实的计算机图形和随附的图形API，即DXR。我们的射线射击第一将给定的OCTREE中的叶级体素映射到一组与轴线对齐的边界盒（AABBS），并使用GPU在它们上使用大量平行的射线射击，以寻找自由和占据的体素。这些被馈回CPU，以更新体素占用率并重组OCTREE。在我们的实验中，我们观察到在最先进的OCTOMAP CPU实现上，使用射线追踪RTX GPU进行射线拍摄方面有超过三端的性能提高，其中基准测试环境由超过77k的点和25k〜34k voxel Grids组成。
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.
