SUMMARYLocalization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

中文翻译：

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使用 RGB-TOF 相机和 IMU 进行工业移动机器人的准确 3D 定位

总结基于视觉自然地标的定位是最先进的自动车辆定位方法之一，但是在快速运动和低纹理环境中受到限制，这可能导致失败。本文提出了一种通过扩展卡尔曼滤波器 (EKF) 解决这些限制的方法，该方法基于状态估计算法，该算法融合了来自低成本 MEMS 惯性测量单元和飞行时间相机的信息。我们在室内环境中展示了我们的结果。我们表明，所提出的方法不需要任何全局反射地标进行定位，并且快速、准确且易于与移动机器人一起使用。