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首页> 外文期刊>IFAC PapersOnLine >Semantic Mapping of Orchards * * This work is supported in part by NRI Award 1525045, RI Large Award 1111638, NSF Award 1317788, USDA Award MIN-98-G02 and the MnDrive initiative.
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Semantic Mapping of Orchards * * This work is supported in part by NRI Award 1525045, RI Large Award 1111638, NSF Award 1317788, USDA Award MIN-98-G02 and the MnDrive initiative.

机译：果园的语义映射 * * 这项工作得到了NRI奖1525045和RI大奖1111638的部分支持，NSF奖1317788，美国农业部奖MIN-98-G02和MnDrive倡议。

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摘要

Abstract: We present a method to construct a semantic map of an apple orchard using a LIDAR and a camera rigidly attached to each other. The system is able to capture the map as a standalone sensor which is light-weight and can be mounted on a variety of platforms. At the geometry level, we present a new method to associate image features captured by the camera with 3D points captured by the LIDAR. We then use this method to register 3D point-clouds onto a common frame. We show that our association method yields superior registration performance compared to common methods which work in indoor or urban settings. At the semantic level, the apples are identified as distinct objects. Their locations and diameters are extracted as relevant attributes. As an example, a semantic map of an orchard row is constructed.
机译：摘要：我们提出了一种方法，该方法使用激光雷达和彼此牢固连接的相机来构建苹果园的语义图。该系统能够将地图捕获为独立的传感器，重量轻，可以安装在各种平台上。在几何级别，我们提出了一种新方法，将相机捕获的图像特征与激光雷达捕获的3D点相关联。然后，我们使用此方法将3D点云注册到公共框架上。我们证明，与在室内或城市环境中运行的常规方法相比，我们的关联方法具有更好的注册性能。在语义级别，苹果被识别为不同的对象。提取它们的位置和直径作为相关属性。作为示例，构建了果园行的语义图。

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