• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation
【2h】

High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation

机译：使用GNSS / IMU / LiDAR传感器集成来支持自动车辆导航的高清3D地图创建

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recent developments in sensor technologies such as Global Navigation Satellite Systems (GNSS), Inertial Measurement Unit (IMU), Light Detection and Ranging (LiDAR), radar, and camera have led to emerging state-of-the-art autonomous systems, such as driverless vehicles or UAS (Unmanned Airborne Systems) swarms. These technologies necessitate the use of accurate object space information about the physical environment around the platform. This information can be generally provided by the suitable selection of the sensors, including sensor types and capabilities, the number of sensors, and their spatial arrangement. Since all these sensor technologies have different error sources and characteristics, rigorous sensor modeling is needed to eliminate/mitigate errors to obtain an accurate, reliable, and robust integrated solution. Mobile mapping systems are very similar to autonomous vehicles in terms of being able to reconstruct the environment around the platforms. However, they differ a lot in operations and objectives. Mobile mapping vehicles use professional grade sensors, such as geodetic grade GNSS, tactical grade IMU, mobile LiDAR, and metric cameras, and the solution is created in post-processing. In contrast, autonomous vehicles use simple/inexpensive sensors, require real-time operations, and are primarily interested in identifying and tracking moving objects. In this study, the main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds. In other words, no other sensor data were considered in this investigation. The results have confirmed that cm-level accuracy can be achieved.
机译：全球导航卫星系统（GNSS），惯性测量单元（IMU），光探测与测距（LiDAR），雷达和摄像机等传感器技术的最新发展催生了新兴的最先进的自主系统，例如无人驾驶车辆或UAS（无人机载系统）群。这些技术需要使用有关平台周围物理环境的准确对象空间信息。通常可以通过传感器的适当选择来提供此信息，包括传感器的类型和功能，传感器的数量及其空间布置。由于所有这些传感器技术都具有不同的错误源和特征，因此需要严格的传感器建模来消除/减轻错误，以获得准确，可靠且强大的集成解决方案。就能够重建平台周围的环境而言，移动地图系统与自动驾驶汽车非常相似。但是，它们在操作和目标上有很大的不同。移动制图车辆使用专业级传感器，例如大地测级GNSS，战术级IMU，移动LiDAR和公制相机，解决方案是在后处理中创建的。相反，自动驾驶车辆使用简单/廉价的传感器，需要实时操作，并且主要对识别和跟踪运动物体感兴趣。在这项研究中，主要目的是评估仅基于Velodyne传感器数据来创建精确点云的自动驾驶车辆传感器系统的性能潜力，以获得高清地图。换句话说，在此调查中未考虑其他传感器数据。结果证实了可以达到厘米级的精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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