Visual ground segmentation by radar supervision
Visual ground segmentation by radar supervision
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DOI：
10.1016/j.robot.2012.10.001
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发表时间：
2014-05-01
影响因子：
4.3
通讯作者：
Douillard, Bertrand
中科院分区：
文献类型：
--
作者：
Milella, Annalisa;Reina, Giulio;Douillard, Bertrand
成像传感器越来越多地用于自动驾驶车辆应用中以进行场景理解。本文提出了一种方法，结合雷达和单目视觉地面建模和场景分割的移动的机器人在室外环境中工作。所提出的系统具有两个主要阶段：雷达监督训练阶段和视觉分类阶段。训练阶段依赖于雷达测量来驱动相机图像中地面块的选择，并在线学习地面的视觉外观。在分类阶段，地面的视觉模型可以用于执行高级任务，如图像分割和地形分类，以及解决雷达模糊。该方法导致以下主要优点：（a）在雷达与相机视场重叠的环境部分上的视觉分类器的自我监督训练。这避免了耗时的人工标记，并且使得能够在线实施;（B）地面模型可以在飞行器的操作期间连续更新，从而使得系统在长距离和长持续时间应用中的使用可行。本文详细介绍了算法，并提出了在该领域进行的实验测试，使用无人驾驶车辆。(C)2012爱思唯尔有限公司版权所有。
Imaging sensors are being increasingly used in autonomous vehicle applications for scene understanding. This paper presents a method that combines radar and monocular vision for ground modeling and scene segmentation by a mobile robot operating in outdoor environments. The proposed system features two main phases: a radar-supervised training phase and a visual classification phase. The training stage relies on radar measurements to drive the selection of ground patches in the camera images, and learn online the visual appearance of the ground. In the classification stage, the visual model of the ground can be used to perform high level tasks such as image segmentation and terrain classification, as well as to solve radar ambiguities. This method leads to the following main advantages: (a) self-supervised training of the visual classifier across the portion of the environment where radar overlaps with the camera field of view. This avoids time-consuming manual labeling and enables on-line implementation; (b) the ground model can be continuously updated during the operation of the vehicle, thus making feasible the use of the system in long range and long duration applications. This paper details the algorithms and presents experimental tests conducted in the field using an unmanned vehicle. (C) 2012 Elsevier B.V. All rights reserved.