Robust Loop Closure Method for Multi-Robot Map Fusion by Integration of Consistency and Data Similarity
Robust Loop Closure Method for Multi-Robot Map Fusion by Integration of Consistency and Data Similarity
复制标题
一致性和数据相似性相结合的多机器人地图融合鲁棒闭环方法
DOI：
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发表时间：
2020
影响因子：
5.2
通讯作者：
Jinwhan Kim
中科院分区：
文献类型：
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作者：
Haggi Do;Seonghun Hong;Jinwhan Kim
为了使多机器人系统在任务中高效协作，系统必须建立全局地图并对其中的机器人进行定位。然而，机器人之间的相对姿态可能是未知的，从而使系统无法生成参考地图。在这种情况下，必须通过机器人间的闭环来推断必要的信息，这主要是机器人观察同一地点时获得的感知衍生测量。然而，由于感知衍生的测量依赖于传感器数据的相似性，如果不同的地方表现出相似的外观，它们可能被错误地识别为相同的位置。这种现象被称为感知混叠，它会产生不准确的循环闭包，从而严重扭曲全局图。本研究提出了一种用于地图融合的鲁棒机器人间环路闭合选择，该选择利用了环路闭合的一致性和数据相似度来进行精确的测量确定。我们将这些信息的组合定义为度量对得分，并将其作为图论中可求解为最大边权团的组合优化问题的目标函数中的权重。该算法在实验数据集上进行了性能评估测试，并与最先进的方法进行了比较。
For an efficient collaboration of multi-robot system during missions, it is essential for the system to create a global map and localize the robots in it. However, the relative poses among robots may be unknown, preventing the system from generating the reference map. In such cases, the necessary information must be inferred through inter-robot loop closures, which are mainly perception-derived measurements obtained when robots observe the same place. However, as perception-derived measurements rely on the similarity of sensor data, different places could be wrongly identified as the same location if they exhibit similar appearances. This phenomenon, called perceptual aliasing, produces inaccurate loop closures that can severely distort the global map. This study presents a robust inter-robot loop closure selection for map fusion that utilizes the degrees of both consistency and data similarity of the loop closures for accurate measurement determination. We define the coalition of these information as the measurement pair score and employ it as weights in the objective function of the combinatorial optimization problem that can be solved as maximum edge weight clique from graph theory. The algorithm is tested on an experimental dataset for performance evaluation and the result is discussed in comparison to a state-of-the-art method.
DOI：
10.1109/icra.2018.8460217
发表时间：
2018-05
期刊：
2018 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
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作者：
Joshua G. Mangelson;Derrick Dominic;R. Eustice;Ram Vasudevan
通讯作者：
Joshua G. Mangelson;Derrick Dominic;R. Eustice;Ram Vasudevan