Mapping of linear road features with the inverse visual detector observation model

Oleg Shipit’ko, A. Kabakov
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Abstract

The paper proposes an algorithm for mapping linear features detected on the roadway — road marking lines, curbs, road boundaries. The algorithm is based on a mapping method with an inverse observation model. An inverse observation model is proposed to take into account the spatial error of the linear feature visual detector. The influence of various parameters of the model on the resulting quality of mapping was studied. The mapping algorithm was tested on data recorded on an autonomous vehicle while driving at the test site. The quality of the mapping algorithm was assessed according to several quality metrics known from the literature. In addition, the mapping problem was considered as a binary classification problem, in which each map cell may or may not contain the desired feature, and the ROC curve and AUC-ROC metric were used to assess the quality. As a naive solution, a map was built containing all detected linear features without any additional filtering. For the map built on the basis of the raw data, the AUC-ROC was 0.75, and as a result of applying the algorithm, the value of 0.81 was reached. The experimental results have confirmed that the proposed algorithm can effectively filter noise and false-positive detections of the detector, which confirms the applicability of the proposed algorithm and the inverse observation model for solving practical problems. Key words Linear features, mapping, inverse observation model, road map, autonomous vehicle, digital road map.
线性道路特征的反演视觉探测器观测模型
本文提出了一种映射在道路上检测到的线性特征的算法——道路标线、路缘、道路边界。该算法基于一种具有逆观测模型的映射方法。为了考虑线性特征视觉检测器的空间误差,提出了一种逆观测模型。研究了模型各参数对映射结果质量的影响。根据自动驾驶汽车在测试现场行驶时记录的数据,对该地图算法进行了测试。根据文献中已知的几个质量度量来评估映射算法的质量。此外,将映射问题视为一个二元分类问题,其中每个映射单元可能包含也可能不包含所需的特征,并使用ROC曲线和AUC-ROC度量来评估质量。作为一种朴素的解决方案,构建了一个包含所有检测到的线性特征的地图,而不需要任何额外的滤波。对于基于原始数据构建的地图,AUC-ROC为0.75,应用该算法后,AUC-ROC为0.81。实验结果证实了所提算法能有效滤除检测器的噪声和假阳性检测,验证了所提算法和逆观测模型在解决实际问题中的适用性。关键词:线性特征,制图,逆观测模型,道路地图,自动驾驶汽车,数字道路地图
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