Spatio-temporal voxel layer: A view on robot perception for the dynamic world
Spatio-temporal voxel layer: A view on robot perception for the dynamic world
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DOI：
10.1177/1729881420910530
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发表时间：
2020-03-01
影响因子：
2.3
通讯作者：
Feinberg, Max
中科院分区：
文献类型：
--
作者：
Macenski, Steve;Tsai, David;Feinberg, Max
时空体素网格是一个积极维护的开源项目，提供改进的三维环境表示，在大型、动态和复杂的环境中得到越来越多的采用。我们提供体素网格和 Costmap 2-D 层插件 Spatio-Temporal Voxel Layer，由实时稀疏占用网格提供支持，可以恒定时间访问体素，而体素不会随环境大小而缩放。我们用一种新的清除技术取代了光线投射，我们称之为截锥体加速，该技术不假设静态环境，并且在实践中可以更好地代表移动环境。与体素层相比，我们的方法在处理 9 个 QVGA 分辨率深度相机时，平均 CPU 负载减少了近 400%。该技术还支持三维激光扫描仪、雷达等传感器以及其他现代传感器，这些传感器以前在可用的 ROS 导航框架中不受支持，而该框架已成为机器人专家工具箱中的主要内容。随着传感器价格的下降和移动计算能力的提高，这些传感器在机器人领域的应用越来越广泛。时空体素层是在其开发生命周期中根据社区反馈公开开发的，并且继续由社区添加其他特性和功能。截至 2019 年 2 月，时空体素层已用于全球仓库、工厂、医院、酒店、商店和图书馆的 600 多个机器人。该开源软件可以在其 GitHub 页面上查看和安装：https://github.com/SteveMacenski/spatio_temporal_voxel_layer。
The spatio-temporal voxel grid is an actively maintained open-source project providing an improved three-dimensional environmental representation that has been garnering increased adoption in large, dynamic, and complex environments. We provide a voxel grid and the Costmap 2-D layer plug-in, Spatio-Temporal Voxel Layer, powered by a real-time sparse occupancy grid with constant time access to voxels which does not scale with the environment's size. We replace ray-casting with a new clearing technique we dub frustum acceleration that does not assume a static environment and in practice, represents moving environments better. Our method operates at nearly 400% less CPU load on average while processing 9 QVGA resolution depth cameras as compared to the voxel layer. This technique also supports sensors such as three-dimensional laser scanners, radars, and additional modern sensors that were previously unsupported in the available ROS Navigation framework that has become staples in the roboticists' toolbox. These sensors are becoming more widely used in robotics as sensor prices are driven down and mobile compute capabilities improve. The Spatio-Temporal Voxel Layer was developed in the open with community feedback over its development life cycle and continues to have additional features and capabilities added by the community. As of February 2019, the Spatio-Temporal Voxel Layer is being used on over 600 robots worldwide in warehouses, factories, hospitals, hotels, stores, and libraries. The open-source software can be viewed and installed on its GitHub page at https://github.com/SteveMacenski/spatio_tem poral_voxel_layer..