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Error Modeling and Calibration of Exteroceptive Sensors for Accurate Mapping Applications

机译：精确测绘应用中感受性传感器的误差建模和校准

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摘要

Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that ' captures the dominant geometric and temporal sources of mapping error. This allows the mapping:accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should be employed whenever maps are formed from range-based exteroceptive sensor data.
机译：可靠的机器人感知和计划对于在不确定，非结构化的环境中执行自主动作至关重要。在现场机器人系统中，自动化是通过解释感受性传感器信息来推断出世界的某些事物而实现的。然后将其映射以提供一致的空间环境，以便可以围绕机器人与世界的预期未来交互来计划动作。整个系统与该链中最薄弱的环节一样可靠。在本文中，“映射”一词被广泛地用来描述基于距离的感受性传感器数据（例如LIDAR或立体视觉）到固定导航框的转换，因此可以用于形成环境的内部表示。分析了从传感器框架到导航框架的坐标转换，以生成空间误差模型，该模型捕获了映射误差的主要几何和时间来源。这允许在运行时计算mapping：accuracy。然后提出了一种基于外感距离传感器的通用外在校准方法，以确定传感器的位置和方向。这样可以将单个传感器中的系统误差降到最低，并且当使用多个传感器时，可以最大程度地减少它们之间的系统矛盾，从而实现可靠的多传感器数据融合。该模型的核心数学推导并不是特别新颖或复杂，但是迄今为止，文献中似乎还缺少对现场机器人技术的严格分析和应用。本文中的技术易于实现，并且极大地提高了映射信息的准确性，准确性和完整性。因此，每当根据基于范围的感受性传感器数据形成地图时，都应采用它们。

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  • 来源
    《Journal of robotic systems》 |2010年第1期|2-20|共19页
  • 作者

    James P. Underwood; Andrew Hill; Thierry Peynot; Steven J. Scheding;

  • 作者单位

    ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The Rose St. Building, J04, University of Sydney, Sydney, New South Wales 2006, Australia;

    ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The Rose St. Building, J04, University of Sydney, Sydney, New South Wales 2006, Australia;

    ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The Rose St. Building, J04, University of Sydney, Sydney, New South Wales 2006, Australia;

    ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics, The Rose St. Building, J04, University of Sydney, Sydney, New South Wales 2006, Australia;

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