Self-learning classification of radar features for scene understanding
Self-learning classification of radar features for scene understanding
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DOI：
10.1016/j.robot.2012.03.002
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发表时间：
2012-11-01
影响因子：
4.3
通讯作者：
Underwood, James
中科院分区：
文献类型：
--
作者：
Reina, Giulio;Milella, Annalisa;Underwood, James
自动驾驶在移动机器人领域是一个具有挑战性的问题，特别是当领域是非结构化的时候，比如在户外环境中。此外，由于照明条件的变化、雾、雨、雪和冰雹等天气现象或灰尘云和烟雾的存在，实地情景的特点也往往是能见度低。因此，越野机器人首先需要先进的感知系统来感知和理解其环境，识别人工和自然结构、拓扑、植被和路径，同时确保在能见度降低的情况下具有健壮性。本文提出使用毫米波雷达作为全天候越野感知的可能解决方案。为了训练用于雷达图像解译和自主导航的分类器，提出了一种自学习方法。该分类器分为两个主要阶段：自适应训练阶段和分类阶段。在训练阶段，系统自动学习将雷达数据的外观与类别标签相关联。然后，它根据过去的观察做出预测。训练集使用最新的雷达读数不断在线更新，从而使该系统在不断变化的环境中用于远程和长持续时间导航是可行的。给出了在农村环境下运行的无人地面车辆的实验结果，以验证该方法的有效性。并与激光数据进行了定量比较，显示出较好的测距精度和测绘能力。最后，对毫米波雷达作为机器人传感器在自然场景中持续和准确感知的效用进行了总结。(C)2012爱思唯尔B.V.保留所有权利。
Autonomous driving is a challenging problem in mobile robotics, particularly when the domain is unstructured, as in an outdoor setting. In addition, field scenarios are often characterized by low visibility as well, due to changes in lighting conditions, weather phenomena including fog, rain, snow and hail, or the presence of dust clouds and smoke. Thus, advanced perception systems are primarily required for an off-road robot to sense and understand its environment recognizing artificial and natural structures, topology, vegetation and paths, while ensuring, at the same time, robustness under compromised visibility. In this paper the use of millimeter-wave radar is proposed as a possible solution for all-weather off-road perception. A self-learning approach is developed to train a classifier for radar image interpretation and autonomous navigation. The proposed classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate the appearance of radar data with class labels. Then, it makes predictions based on past observations. The training set is continuously updated online using the latest radar readings, thus making it feasible to use the system for long range and long duration navigation, over changing environments. Experimental results, obtained with an unmanned ground vehicle operating in a rural environment, are presented to validate this approach. A quantitative comparison with laser data is also included showing good range accuracy and mapping ability as well. Finally, conclusions are drawn on the utility of millimeter-wave radar as a robotic sensor for persistent and accurate perception in natural scenarios. (C) 2012 Elsevier B.V. All rights reserved.