CN115049863A - 一种基于深度学习的点云匹配滤波方法 - Google Patents

一种基于深度学习的点云匹配滤波方法 Download PDF

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CN115049863A
CN115049863A CN202210599666.8A CN202210599666A CN115049863A CN 115049863 A CN115049863 A CN 115049863A CN 202210599666 A CN202210599666 A CN 202210599666A CN 115049863 A CN115049863 A CN 115049863A
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岳增琪
李裕家
雷多加
张建国
贺伟
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Gansu Dayu Jiuzhou Space Information Technology Co ltd
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Abstract

本发明涉及地理测绘技术领域，其目的在于提供了一种基于深度学习的点云匹配滤波方法，包括如下步骤：首先基于神经网络模型VGG进行可见光图像的特征识别分类，有效区分地表性质特征；然后基于卡尔曼滤波算法进行点云优化，将点云通过网格与三角形组合的多次构网算法解算的成果与图像分类的成果进行高度匹配，实现特征选择提取，完成三维点云的精细化滤波分类，根据真实的地面点信息进行高精度地表三维场景重建。本发明充分结合神经网络模型VGG与卡尔曼滤波算法，通过先还原拍摄地点的原始图像信息，然后去除云点数据中的噪声干扰，进而能够获得真实的地面点信息，实现地表三维场景的高精度重建，有效提高了精准度，满足了测绘要求。

Description

一种基于深度学习的点云匹配滤波方法
技术领域
本发明属于地理测绘技术领域，具体涉及一种基于深度学习的点云匹配滤波方法。
背景技术
随着3D采集技术发展，三维点云广泛应用于自动驾驶、机器人、遥感和医疗等领域，而三维点云配准是其中的关键任务。三维点云成像一般利用激光测距仪将距离信息和方位坐标融合，生成具有详细距离信息的目标三维点云数据。利用三维点云可以在有限时间内获取大范围区域的高精度三维地形数据，而且激光脉冲能部分地穿透植被遮挡，从而获取地面高程并重建出地表三维地形，甚至可以反演出地表植被演化，相比传统测绘具有十分巨大的优势。
然而统线性探测激光雷达受限于目标本身反射率较低或系统激光能量和探测器灵敏度较低，导致激光雷达所成的图像分辨率较低，建立三维场景精度较低，达不到测绘要求。
发明内容
本发明的目的在于提供一种基于深度学习的点云匹配滤波方法，以解决上述背景技术中提出的问题。
为了实现上述目的，本发明采用的技术方案是：
一种基于深度学习的点云匹配滤波方法，包括如下步骤：
S1：采用无人机拍摄地面影像，基于神经网络模型VGG对拍摄影像进行可见光图像的特征识别分类，区分地表性质特征，获得点云数据；
S2：基于卡尔曼滤波算法进行点云优化，将点云通过网格与三角形组合的多次构网算法解算的成果与图像分类的成果进行高度匹配，实现特征选择提取，完成三维点云的精细化滤波分类，获得真实的地面点信息；
S3：根据真实的地面点信息进行高精度地表三维场景重建。
综上所述，由于采用了上述技术方案，本发明的有益效果是：
通过采用神经网络模型VGG进行可见光图像的特征识别分类，能够还原拍摄地点的原始图像信息，同时利用卡尔曼滤波算法对点云数据信息进行优化，能够去除云点数据中的噪声干扰，进而能够获得真实的地面点信息，实现地表三维场景的高精度重建，有效提高了精准度，满足了测绘要求。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。通常在此处描述和示出的本发明实施例的组件可以以各种不同的配置来布置和设计。
因此，以下对提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围，而是仅仅表示本发明的选定实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例1
本发明所述的一种基于深度学习的点云匹配滤波方法，包括如下步骤：
S1：采用无人机在空中进行地面影像拍摄，获得拍摄点的拍摄影像；
S2：采用神经网络模型VGG对拍摄影像进行可见光图像的特征识别分类，还原拍摄点原始图像信息，区分地表性质特征，得到点云数据；
S3：由于点云数据中存在大量噪声，会影响数据真实性，因此采用卡尔曼滤波算法进行点云优化，将点云通过网格与三角形组合的多次构网算法解算的成果与图像分类的成果进行高度匹配，去除云点数据中的噪声干扰，实现特征选择提取，完成三维点云的精细化滤波分类，获得真实的地面点信息；
S4：根据真实的地面点信息进行高精度地表三维场景重建。

Claims (1)

1.一种基于深度学习的点云匹配滤波方法，其特征在于，包括如下步骤：
S1：采用无人机拍摄地面影像，基于神经网络模型VGG对拍摄影像进行可见光图像的特征识别分类，区分地表性质特征，获得点云数据；
S2：基于卡尔曼滤波算法进行点云优化，将点云通过网格与三角形组合的多次构网算法解算的成果与图像分类的成果进行高度匹配，实现特征选择提取，完成三维点云的精细化滤波分类，获得真实的地面点信息；
S3：根据真实的地面点信息进行高精度地表三维场景重建。
CN202210599666.8A 2022-05-30 2022-05-30 一种基于深度学习的点云匹配滤波方法 Pending CN115049863A (zh)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116664790A (zh) * 2023-07-26 2023-08-29 昆明人为峰科技有限公司 基于无人机测绘的三维地形分析系统及方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116664790A (zh) * 2023-07-26 2023-08-29 昆明人为峰科技有限公司 基于无人机测绘的三维地形分析系统及方法
CN116664790B (zh) * 2023-07-26 2023-11-17 昆明人为峰科技有限公司 基于无人机测绘的三维地形分析系统及方法

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