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POLYGON-BASED 3D SCAN REGISTRATION WITH DUAL-ROBOTS IN STRUCTURED INDOOR ENVIRONMENTS

机译:结构室内环境中基于双机器人的基于多边形的3D扫描配准

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This paper addresses the problems involved in 3D mapping in structured indoor environments. We explore a scenario where a dual-robot system jointly constructs the complete 3D map of an indoor environment. We assume that the environment is partially planar; lacking visual features and the robot odometry is unreliable. We introduce a framework to obtain the 6-degree of freedom relative pose between two partially overlapping range images acquired by the dual robots in stop-n-go fashion. Finally, the relative pose of the dual robots and their respective range images are transformed into a global frame to obtain a complete 3D map. The relative pose estimate between the two partially overlapping range maps acquired by the dual robots at any given instance is obtained in two steps, viz, a camera pose estimation algorithm that estimates the initial pose between the two range images and a 3D scan registration algorithm that further refines the pose estimate between the partially overlapping range images. The novel scan registration algorithm presented in this paper is initialized by a good pose estimate and registers two polygon sets extracted from the range images acquired by the dual robots. The experiments are intended to evaluate quantitatively the pose parameters for both the camera pose estimation algorithm and the scan registration algorithm. We experimentally validate our method with loop closure resulting in an accurate polygon map of an indoor environment.
机译:本文解决了结构化室内环境中3D映射所涉及的问题。我们探讨了双机器人系统共同构建室内环境的完整3D地图的场景。我们假设环境是部分平面的;缺乏视觉特征,机器人的测距不可靠。我们引入了一个框架,以获取双机器人以停停走走方式获取的两个部分重叠的距离图像之间的6自由度相对姿势。最后,将双机器人的相对姿势及其各自的距离图像转换为全局框架,以获得完整的3D地图。双机器人在任何给定实例下获取的两个部分重叠的距离图之间的相对姿势估计是通过两个步骤获得的,即,估计两个距离图像之间的初始姿势的相机姿势估计算法和一个3D扫描配准算法,进一步完善部分重叠范围图像之间的姿态估计。本文提出的新型扫描配准算法通过良好的姿态估计进行初始化,并配准从双机器人获取的距离图像中提取的两个多边形集。实验旨在为相机姿态估计算法和扫描配准算法定量评估姿态参数。我们通过闭环实验来验证我们的方法,从而得出室内环境的准确多边形图。

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