论文标题
计算结合和低频带宽分布分布3D图形 - 萨克
Compute-Bound and Low-Bandwidth Distributed 3D Graph-SLAM
论文作者
论文摘要
本文介绍了一种新的分布式3D SLAM地图构建方法。本文的关键贡献是创建了针对机器人平台的带宽和计算需求的分布式图形 - 链接映射构建结构。通过将3D点云与平面云压缩算法的集成来提供响应能力，该算法使用局部平面贴片近似于密集的3D点云。计算绑定的平台可能会限制压缩算法的计算持续时间，而低带宽平台可以限制压缩结果的大小。该方法的主干是一种超快速的自适应3D压缩算法，该算法将3D平面表面数据的附带转换为带有图像纹理的平面贴片。我们的方法使用DVO SLAM，这是一种用于3D映射的领先算法，并通过将MAP集成任务与本地指导，导航和控制任务进行计算隔离，并包括添加网络协议以共享压缩平面云。这些贡献的共同效果使具有3D传感功能的代理可以计算和传达压缩地图信息与其机上计算资源和通信渠道能力相称的压缩地图信息。这打开了大量映射到机器人平台的新类别，这些平台可能具有禁止其他SLAM解决方案的计算和内存限制。
This article describes a new approach for distributed 3D SLAM map building. The key contribution of this article is the creation of a distributed graph-SLAM map-building architecture responsive to bandwidth and computational needs of the robotic platform. Responsiveness is afforded by the integration of a 3D point cloud to plane cloud compression algorithm that approximates dense 3D point cloud using local planar patches. Compute bound platforms may restrict the computational duration of the compression algorithm and low-bandwidth platforms can restrict the size of the compression result. The backbone of the approach is an ultra-fast adaptive 3D compression algorithm that transforms swaths of 3D planar surface data into planar patches attributed with image textures. Our approach uses DVO SLAM, a leading algorithm for 3D mapping, and extends it by computationally isolating map integration tasks from local Guidance, Navigation, and Control tasks and includes an addition of a network protocol to share the compressed plane clouds. The joint effect of these contributions allows agents with 3D sensing capabilities to calculate and communicate compressed map information commensurate with their onboard computational resources and communication channel capacities. This opens SLAM mapping to new categories of robotic platforms that may have computational and memory limits that prohibit other SLAM solutions.
