Geomatics Multi-Sensor Systems

在过去的十年中，自动驾驶车辆系统（AVS）的使用在军事和政府机构、工业和学术界持续增长。在这些AVS中，无人驾驶汽车（SDV）和无人机（UAV）受到了众多用户最全面的关注，人们对AVS的信任取决于其自主性的可靠性，包括这类车辆的定位、定位和导航（Positioning，Location And Navigation，简称GPS）关键任务。AVS通常依赖于基于卫星的全球导航卫星系统（GNSS）（诸如GPS）作为核心定位技术。然而，GNSS的性能在诸如信号阻塞、多径和信号干扰的劣化环境中受到极大影响，并且因此当前AVS的导航更严重地依赖于数字地图和遥感传感器（例如，激光检测和测距（LiDAR）、相机、雷达）来感知环境。然而，这些传感器也会在低能见度、雨和雾等退化的环境条件下受到影响。第三类是独立于环境的导航传感器，例如惯性导航系统（INS），其使用加速度计和陀螺仪测量运动动态。这些传感器可以在任何地方以非常高的速率提供数据，而不受天气条件或外部信号或基础设施的影响。然而，在没有来自外部源的更新的情况下，它们的性能随着时间而降低。因此，现有的AVS解码技术各有优缺点; CRC研究计划将促进技术和处理平台的发展，使新的GNSS系统能够在所有环境中有效地运行，特别是在诸如GNSS信号阻塞、黑暗、没有视觉特征/地标、烟、雾和雨。 这将通过所有可用传感器的多传感器融合来实现，以在任何地方和所有环境中提供连续和稳健的定位，以及机器智能算法来管理和协调传感器/系统的异构集合，以满足安全关键自主导航的需求。从自治的角度来看，主席的工作预计将有很大的影响，接受和信任的AVS，因为它将增加其可靠性。

During the past decade, the use of Autonomous Vehicles Systems (AVS) has experienced continued growth across military and governmental agencies, industry, and academia. Among these AVS, Self-Driving Vehicles "SDV" and Unmanned Aerial Vehicles "UAV" have drawn the most comprehensive attention from many users.The trust of AVS hinges on the reliability of the autonomy including the crucial task of Positioning, Location And Navigation (PLAN) of such vehicles. AVS typically rely on satellite-based Global Navigation Satellite Systems (GNSS), such as GPS, as the core positioning technology. However, the performance of GNSS greatly suffers in degraded environments such as signal blockage, multipath and signal jamming and as a result navigation of current AVS relies more heavily on digital maps and remote sensing sensors (e.g. laser detection and ranging (LiDAR), Cameras, RADAR) to perceive the environment. However, these sensors also suffer in degraded environmental conditions such as low-visibility, rain, and fog. A third category are self-contained/environment-independent navigation sensors such as Inertial Navigation Systems (INS) which measure the motion dynamics using accelerometers and gyroscopes. These sensors can provide PLAN everywhere at a very high rate independent of weather conditions or external signals or infrastructure. However, their performance degrades with time in the absence of updates from external sources. As such, current PLAN techniques for AVS all have advantages and drawbacks; there is no single technique that appears to be sufficiently versatile to address the challenges mentioned above.The CRC research program will advance the development of technologies and processing platforms that enable the deployment of novel PLAN systems operating efficiently in all environments and specifically in degraded environments conditions such as GNSS Signal blockage, darkness, absence of visual features/landmarks, smoke, fog and rain. This will be achieved through multi-sensor fusion of all available sensors to provide continuous and robust positioning anywhere and in all environments and machine intelligence algorithms to manage and coordinate the heterogeneous set of sensors/systems into a robust positioning system to meet the demand for safety-critical autonomous navigation. From autonomy perspective, the chair work is expected to have a large impact on the acceptance and trust of AVS as it will increase its reliability.