Artificial landmark-based underwater localization for AUVs using weighted template matching
Artificial landmark-based underwater localization for AUVs using weighted template matching
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DOI：
10.1007/s11370-014-0153-y
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发表时间：
2014-07-01
影响因子：
2.5
通讯作者：
Choi, Hyun-Taek
中科院分区：
文献类型：
--
作者：
Kim, Donghoon;Lee, Donghwa;Choi, Hyun-Taek
本文研究了结构化水下环境中基于视觉的定位技术。对于水下机器人来说，精确定位是完成复杂任务的必要条件，但目前能够在水下环境中实现精确定位的传感器很少。在可用的传感器中，相机在执行短距离任务时非常有用，尽管水下条件恶劣，包括低能见度，噪音和大面积无特征场景。为了解决这些问题，我们设计了一种用于相机定位的人工地标，并提出了一种新的基于视觉的目标检测技术，并将其应用于蒙特卡洛定位（MCL）算法和基于地图的定位技术。在图像处理步骤中，提出了一种新的加权和相关系数、基于多模板的目标选择和基于颜色的图像分割方法。在定位步骤中，为了将地标检测结果应用于MCL，将航位推算信息和地标检测结果分别用于预测和更新阶段。通过水下机器人平台的实验，对该方法的性能进行了评价，并对实验结果进行了讨论。
This paper deals with vision-based localization techniques in structured underwater environments. For underwater robots, accurate localization is necessary to perform complex missions successfully, but few sensors are available for accurate localization in the underwater environment. Among the available sensors, cameras are very useful for performing short-range tasks despite harsh underwater conditions including low visibility, noise, and large areas of featureless scene. To mitigate these problems, we design artificial landmarks to be utilized with a camera for localization, and propose a novel vision-based object detection technique and apply it to the Monte Carlo localization (MCL) algorithm, amap-based localization technique. In the image processing step, a novel correlation coefficient using a weighted sum, multiple-template-based object selection, and color-based image segmentation methods are proposed to improve the conventional approach. In the localization step, to apply the landmark detection results to MCL, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform and the results are discussed.