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首页> 外文会议>International Conference on Informatics in Control, Automation and Robotics >Identifying landmark cues with LIDAR laser scanner data taken from multiple viewpoints
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Identifying landmark cues with LIDAR laser scanner data taken from multiple viewpoints

机译：使用LIDAR激光扫描仪数据从多个角度识别地标线索

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In this paper, we report on our ongoing efforts to build a cue identifier for mobile robot navigation using a simple one-plane LIDAR laser scanner and machine learning techniques. We used simulated scans of environmental cues to which we applied various levels of Gaussian distortion to test a number of models the effectiveness of training and the response to noise in input data. We concluded that in contrast to back propagation neural networks, SVM-based models are very well suited for classifying cues, even with substantial Gaussian noise, while still preserving efficiency of training even with relatively large data sets. Unfortunately, models trained with data representing just one stationary point of view of a cue are inaccurate when tested on data representing different points of view of the cue. Although the models are resilient to noisy data coming from the vicinity of the original point of view used in training, data that originates in a point of view shifted forward or backward (as would be the case with a mobile robot) proved much more difficult to classify correctly. In the research reported here, we used an expanded set of synthetic training data representing three view points corresponding to three positions in robot movement in relation to the location of the cues. We show that by using the expanded data the accuracy of cue classification is dramatically increased for test data coming from any of the points.
机译：在本文中，我们报告了我们为使用简单的单平面LIDAR激光扫描仪和机器学习技术为移动机器人导航建立提示标识符所做的努力。我们使用了模拟的环境提示扫描，并对其应用了各种高斯失真度，以测试许多模型的训练效果以及输入数据中对噪声的响应。我们得出的结论是，与反向传播神经网络相比，基于SVM的模型非常适用于对线索进行分类，即使存在大量的高斯噪声，即使在具有相对较大的数据集的情况下，仍然可以保持训练的效率。不幸的是，用仅代表提示的一个固定视角的数据训练的模型在针对代表提示的不同视角的数据进行测试时是不准确的。尽管模型可以适应来自训练中原始视点附近的嘈杂数据，但事实证明，源自视点向前或向后移动的数据（如移动机器人的情况）要困难得多正确分类。在这里报告的研究中，我们使用了一组扩展的综合训练数据，这些数据代表了三个视点，它们对应于机器人相对于提示位置的三个位置。我们显示，通过使用扩展的数据，对于来自任何点的测试数据，提示分类的准确性都会大大提高。

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