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_temporal_voxel_layer.

中文翻译：

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时空体素层：动态世界的机器人感知视图

时空体素网格是一个积极维护的开源项目，它提供了一种改进的三维环境表示，在大型、动态和复杂的环境中得到了越来越多的采用。我们提供了一个体素网格和 Costmap 2-D 层插件时空体素层，由实时稀疏占用网格提供支持，该网格具有对不随环境大小缩放的体素的恒定时间访问。我们用一种新的清除技术代替了光线投射，我们称之为平截头体加速，它不假设静态环境，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，平均减少了近 400% 的 CPU 负载。该技术还支持传感器，如三维激光扫描仪、雷达、以及以前在可用的 ROS 导航框架中不受支持的其他现代传感器，这些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技术中的应用越来越广泛。时空体素层是在公开开发的，社区对其开发生命周期的反馈，并继续具有社区添加的其他特性和功能。截至 2019 年 2 月，时空体素层已被用于全球仓库、工厂、医院、酒店、商店和图书馆的 600 多个机器人。可以在其 GitHub 页面 https://github.com/SteveMacenski/spatio_temporal_voxel_layer 上查看和安装开源软件。