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Cleaning robot navigation using panoramic views and particle clouds as landmarks

机译:使用全景视图和粒子云作为地标来清洁机器人导航

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摘要

The paper describes a visual method for the navigation of autonomous floor-cleaning robots. The method constructs a topological map with metrical information where place nodes are characterized by panoramic images and by particle clouds representing position estimates. Current image and position estimate of the robot are interrelated to landmark images and position estimates stored in the map nodes through a holistic visual homing method which provides bearing and orientation estimates. Based on these estimates, a position estimate of the robot is updated by a particle filter. The robot's position estimates are used to guide the robot along parallel, meandering lanes and are also assigned to newly created map nodes which later serve as landmarks. Computer simulations and robot experiments confirm that the robot position estimate obtained by this method is sufficiently accurate to keep the robot on parallel lanes, even in the presence of large random and systematic odometry errors. This ensures an efficient cleaning behavior with almost complete coverage of a rectangular area and only small repeated coverage. Furthermore, the topological-metrical map can be used to completely cover rooms or apartments by multiple meander parts.
机译:本文介绍了一种用于自主地板清洁机器人导航的可视化方法。该方法构建具有度量信息的拓扑图,其中位置节点的特征是全景图像和代表位置估计值的粒子云。机器人的当前图像和位置估计与地标图像和通过提供方位和方向估计的整体视觉归位方法存储在地图节点中的位置估计相关。基于这些估计,可以通过粒子过滤器更新机器人的位置估计。机器人的位置估计值用于沿平行的蜿蜒车道引导机器人,并且还分配给新创建的地图节点,这些地图节点以后将用作地标。计算机仿真和机器人实验证实,即使在存在较大的随机和系统测距误差的情况下,通过这种方法获得的机器人位置估计值也足够准确,可以将机器人保持在平行车道上。这样可以确保有效的清洁行为,几乎可以完全覆盖矩形区域,而只需很小的重复覆盖即可。此外,拓扑度量图可用于通过多个曲折部分完全覆盖房间或公寓。

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