基于Orb-SLAM2和OpenMVS的快速原位网格生成

Thomas Wright, Toshihide Hanari, K. Kawabata, B. Lennox
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引用次数: 3

摘要

在核退役的探索性机器人中,环境理解是关键。福岛第一核电站(Fukushima Daiichi Power Station)和塞拉菲尔德(Sellafield)等核电站经常使用人工控制或半自动车辆进行勘探和监测。在许多情况下,机器人具有有限的传感能力,例如单个摄像机向操作员提供视频。这些限制可能会导致问题,其中缺乏关于环境的数据和对图像深度的有限理解可能导致对资产状态的错误理解或对机器人或环境造成的潜在损害。这项工作的目的是通过使用有限的传感器(即单个单目摄像机)来帮助操作员,以便在不需要卸载大量数据进行处理的情况下,原位生成机器人周围环境的估计。然后,这些信息可以显示为网格,并在3D中进行操作,以提高操作员的意识。由于操作的目标环境是放射性的,速度优先于准确性,因为辐射会对电子设备造成破坏性影响。在光照良好的环境中,图像可以叠加到网格上,以提高操作员的理解能力,并为网格添加细节。从结果来看,环境/对象的3D网格可以在可接受的时间框架内生成,少于5分钟。这与当前许多方法不同,这些方法由于摄影测量的大量计算需求而需要离线处理,或者作为原始点云提供的数据信息量远远不足,难以解释。由于生成速度快,所提出的技术允许较低分辨率的网格,以避免在任务期间生成的环境中发生碰撞,然而,在这种技术准备部署之前,仍有几个问题需要解决。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast In-situ Mesh Generation using Orb-SLAM2 and OpenMVS
In exploratory robotics for nuclear decommissioning, environmental understanding is key. Sites such as Fukushima Daiichi Power Station and Sellafield often use manually controlled or semi-autonomous vehicles for exploration and monitoring of assets. In many cases, robots have limited sensing capabilities such as a single camera to provide video to the operators. These limitations can cause issues, where a lack of data about the environment and limited understanding of depth within the image can lead to a mis-understanding of asset state or potential damage being caused to the robot or environment. This work aims to aid operators by using the limited sensors provided i.e. a single monocular camera, to allow estimates of the robot’s surrounding environments to be generated insitu without having to off load large amounts of data for processing. This information can then be displayed as a mesh and manipulated in 3D to improve the operator awareness. Due to the target environment for operation being radioactive, speed is prioritised over accuracy, due to the damaging effects radiation can cause to electronics. In well lit environments images can be overlaid onto the meshes to improve the operators understanding and add detail to the mesh. From the results it has been found that 3D meshes of an environment/object can be generated in an acceptable time frame, less than 5 minutes. This differs from many current methods which require offline processing due to heavy computational requirement of Photogrammetry, or are far less informative giving data as raw point clouds, which can be hard to interpret. The proposed technique allows for lower resolution meshes good enough for avoiding collisions within an environment to be generated during a mission due to it’s speed of generation, however there are still several issues which need to be solved before such a technique is ready for deployment.
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