• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Future generation computer systems >The intelligent obstacle sensing and recognizing method based on D-S evidence theory for UGV
【24h】

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智能障碍物感知与识别方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

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：无人地面车辆（UGV）已广泛用于物联网，军事应用和外太空探索。可靠的智能障碍物识别是无人飞行器的关键要求和重大挑战。为了在所有情况下都能进行可靠的障碍物识别，基于多个传感器的数据处理和融合将是必要的。本文通过从相机和激光扫描仪中提取障碍物特征证据来进行障碍物传感和识别研究。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证据理论可识别当前障碍。测试结果证明了该方法的有效性及其在越野环境感知中的应用，也为包括安全性控制和辅助驾驶技术在内的车辆互联网提供了参考，相关算法也有可能在物联网或物联网中应用。大数据处理。 （C）2019 Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号