Real-time SLAM with octree evidence grids for exploration in underwater tunnels
Real-time SLAM with octree evidence grids for exploration in underwater tunnels
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DOI：
10.1002/rob.20165
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发表时间：
2007-01-01
影响因子：
8.3
通讯作者：
Wettergreen, David
中科院分区：
文献类型：
--
作者：
Fairfield, Nathaniel;Kantor, George;Wettergreen, David
我们描述了一种用于探索水下洞穴和隧道的悬停水下航行器的同步定位和测绘（SLAM）方法，这是一个真正的三维（3D）环境。我们的方法包括一个带有三维证据网格图表示的rao - blackwelzed粒子滤波器。我们描述了一个动态调整粒子数量以提供实时性能的过程。我们还描述了如何调整粒子滤波预测步骤以适应传感器退化或故障。我们提出了一种有效的八叉树数据结构，使其能够维护粒子过滤器所需的数百个映射，以准确地模拟大型环境。这种八叉树结构可以利用粒子之间的空间局域性和时间共享祖先来减少处理和存储需求。为了测试我们的SLAM方法，我们使用了在佛罗里达州的Wakulla Springs洞穴系统和墨西哥的Sistema Zacaton手动部署的声纳测绘车收集的数据，以及在奥斯汀应用研究实验室的测试罐中由DEPTHX车辆收集的数据。我们用这些真实世界的数据集展示了我们的映射和定位方法。(c) 2007 Wiley期刊公司
We describe a simultaneous localization and mapping (SLAM) method for a hovering underwater vehicle that will explore underwater caves and tunnels, a true three-dimensional (3D) environment. Our method consists of a Rao-Blackwellized particle filter with a 3D evidence grid map representation. We describe a procedure for dynamically adjusting the number of particles to provide real-time performance. We also describe how we adjust the particle filter prediction step to accommodate sensor degradation or failure. We present an efficient octree data structure that makes it feasible to maintain the hundreds of maps needed by the particle filter to accurately model large environments. This octree structure can exploit spatial locality and temporal shared ancestry between particles to reduce the processing and storage requirements. To test our SLAM method, we utilize data collected with manually deployed sonar mapping vehicles in the Wakulla Springs cave system in Florida and the Sistema Zacaton in Mexico, as well as data collected by the DEPTHX vehicle in the test tank at the Austin Applied Research Laboratory. We demonstrate our mapping and localization approach with these real-world datasets. (c) 2007 Wiley Periodicals, Inc.