Active Visual SLAM with Independently Rotating Camera

Elia Bonetto, Pascal Goldschmid, Michael J. Black, Aamir Ahmad
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引用次数: 3

Abstract

In active Visual-SLAM (V-SLAM), a robot relies on the information retrieved by its cameras to control its own movements for autonomous mapping of the environment. Cameras are usually statically linked to the robot’s body, limiting the extra degrees of freedom for visual information acquisition. In this work, we overcome the aforementioned problem by introducing and leveraging an independently rotating camera on the robot base. This enables us to continuously control the heading of the camera, obtaining the desired optimal orientation for active V-SLAM, without rotating the robot itself. However, this additional degree of freedom introduces additional estimation uncertainties, which need to be accounted for. We do this by extending our robot’s state estimate to include the camera state and jointly estimate the uncertainties. We develop our method based on a state-of-the-art active V-SLAM approach for omnidirectional robots and evaluate it through rigorous simulation and real robot experiments. We obtain more accurate maps, with lower energy consumption, while maintaining the benefits of the active approach with respect to the baseline. We also demonstrate how our method easily generalizes to other non-omnidirectional robotic platforms, which was a limitation of the previous approach. Code and implementation details are provided as open-source.
具有独立旋转相机的主动视觉SLAM
在主动视觉slam (V-SLAM)中,机器人依靠摄像头获取的信息来控制自己的运动,从而自主地绘制环境地图。摄像机通常静态地连接在机器人的身体上,限制了视觉信息获取的额外自由度。在这项工作中,我们通过在机器人基座上引入和利用一个独立旋转的摄像机来克服上述问题。这使我们能够持续控制摄像机的方向,获得主动V-SLAM所需的最佳方向,而无需旋转机器人本身。然而,这个额外的自由度引入了额外的估计不确定性,这需要加以考虑。我们通过扩展机器人的状态估计来包括相机状态并联合估计不确定性来做到这一点。我们基于最先进的全向机器人主动V-SLAM方法开发了我们的方法,并通过严格的仿真和真实机器人实验对其进行了评估。我们以更低的能耗获得更精确的地图,同时保持主动方法相对于基线的优势。我们还演示了我们的方法如何轻松地推广到其他非全向机器人平台,这是以前方法的局限性。代码和实现细节作为开源提供。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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