LIDAR and stereo combination for traversability assessment of off-road robotic vehicles
LIDAR and stereo combination for traversability assessment of off-road robotic vehicles
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DOI：
10.1017/s0263574715000442
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发表时间：
2016-12-01
期刊：
影响因子：
2.7
通讯作者：
Worst, Rainer
中科院分区：
文献类型：
--
作者：
Reina, Giulio;Milella, Annalisa;Worst, Rainer
使用多传感器输入对地形可穿越能力进行可靠评估是驾驶自动化的关键问题，特别是当领域是非结构化或半结构化的时候，如在自然环境中。本文提出了利用激光雷达和立体声相结合的方法来探测室外可穿越地面的方法。该系统集成了两个自学习分类器，一个基于LIDAR数据，另一个基于立体数据，以检测广泛的可驾驶地面类别。每个单传感器分类器有两个主要阶段：自适应训练阶段和分类阶段。在训练阶段，分类器自动学习将3D数据的几何外观与类别标签相关联。然后，它根据过去的观察做出预测。从单传感器分类器获得的输出被统计地组合在一起，以便利用它们各自的优势，并达到比单独使用每个分类器所能实现的总体性能更好的性能。在农村环境下运行的试验台上获得的实验结果验证和评估了该方法的性能，显示了该方法在户外环境下自主导航的有效性和潜在适用性。
Reliable assessment of terrain traversability using multi-sensory input is a key issue for driving automation, particularly when the domain is unstructured or semi-structured, as in natural environments. In this paper, LIDAR-stereo combination is proposed to detect traversable ground in outdoor applications. The system integrates two self-learning classifiers, one based on LIDAR data and one based on stereo data, to detect the broad class of drivable ground. Each single-sensor classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the classifier automatically learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers are statistically combined in order to exploit their individual strengths and reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in rural environments, are presented to validate and assess the performance of this approach, showing its effectiveness and potential applicability to autonomous navigation in outdoor contexts.