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The intelligent obstacle sensing and recognizing method based on D-S evidence theory for UGV

机译：基于D-S证据理论的UGV智能障碍传感和识别方法

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摘要

Unmanned Ground Vehicles (UGVs) have been widely used in Internet of Things, military applications and outer space exploration. Reliable intelligent obstacle recognition is the key requirement and significant challenge for UGVs. To perform robust obstacle recognition under all conditions, data processing and fusing will be necessary based on multiple sensors. This paper carries out obstacle sensing and recognizing research by extracting obstacle feature evidence from camera and laser scanner. The feature evidence extracted from camera includes S/V in HSV color space. The laser feature evidences are related to penetrability value feature and lambda(3)/lambda(2) feature based on covariance matrix. Then, the key step is to calculate the basic probability assignment based on these feature evidences. By applying Dempster's combination rules the current obstacle is recognized based on D-S evidence theory. The test results show the validity of this new method and its application in cross-country environment perception, also give references to Internet of Vehicles including vehicle safety control and assistance driving technology, also the related algorithm can be potentially used in the Internet of Things or big data processing. (C) 2019 Elsevier B.V. All rights reserved.
机译：无人机地面车辆（UGV）已广泛用于物联网，军事应用和外层空间勘探。可靠的智能障碍识别是UGV的关键要求和重大挑战。为了在所有条件下执行强大的障碍识别，基于多个传感器将需要数据处理和熔化。本文通过从相机和激光扫描仪提取障碍物特征证据来进行障碍物感应和识别研究。从相机中提取的特征证据包括HSV颜色空间中的S / V.激光特征证据与基于协方差矩阵的渗透性值特征和Lambda（3）/ Lambda（2）特征有关。然后，关键步骤是基于这些特征证据来计算基本概率分配。通过应用Dempster的组合规则，基于D-S证据理论来识别目前的障碍。测试结果表明，这种新方法的有效性及其在跨国环境感知中的应用，还给出了包括车辆安全控制和辅助驾驶技术的车辆互联网的参考资料，也可以在有关算法中使用相关算法或大数据处理。 （c）2019 Elsevier B.v.保留所有权利。

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