Feature extraction and data association for AUV concurrent mapping and localisation
Feature extraction and data association for AUV concurrent mapping and localisation
复制标题
AUV并发测绘和定位的特征提取和数据关联
DOI：
10.1109/robot.2001.933044
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
Cedric Salson
中科院分区：
文献类型：
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作者：
I. T. Ruiz;Y. Pétillot;D. Lane;Cedric Salson
描述了一种适用于自主水下航行器(AUV)定位的并行测绘与定位(CML)算法。提出的CML算法使用标准的现成声纳来感知环境。声纳的回波被用来探测车辆附近的目标。CML算法将这些目标与车辆模型结合使用，以同时构建环境的绝对地图并在绝对坐标中定位车辆。为了使算法起作用，存储的目标必须在每次迭代时与声纳回波相关联。考虑到声纳数据的性质，虚假返回使这一过程变得复杂。因此，目标的选择和适当的数据关联策略至关重要。选定的目标由实力雄厚的回报组成。分割检测这些目标并计算(A)它们的质心相对于车辆的相对位置，(B)目标的表面大小，以及(C)目标的第一不变矩。该信息由系统用来执行数据关联。我们选择将众所周知的多假设跟踪滤波器(MHTF)适应于CML结构。这是一种面向测量的方法，它计算确定的目标产生一定回报的概率。文中给出了实际声纳数据的计算结果。
This paper describes a concurrent mapping and localisation (CML) algorithm suitable for localising an autonomous underwater vehicle (AUV). The proposed CML algorithm uses a standard off-the-shelf sonar for sensing the environment. The returns from the sonar are used to detect targets in the vehicle's vicinity. These targets are used in conjunction with a vehicle model by the CML algorithm to concurrently build an absolute map of the environment and localise the vehicle in absolute coordinates. In order for the algorithm to work, the stored targets must be associated to the sonar returns at each iteration. Given the nature of sonar data, false returns complicate this process. The choice of targets and a suitable data association strategy is, therefore, vital. The chosen targets consist of returns of a significant strength. The segmentation detects these targets and calculates (a) the relative position of their center of mass with respect to the vehicle, (b) the targets' surface size, and (c) the targets' first invariant moment. This information is used by the system to perform the data association. We have chosen to adapt the well known multiple hypothesis tracking filter (MHTF) to the CML structure. This is a measurement oriented approach that finds the probability that an established target gave rise to a certain return. The paper presents results with real sonar data.