基于机舱式多普勒脉冲激光雷达测风的风速重构技术

近年来,激光雷达测风已经成为一种可靠的风速测量技术,为了在风电机组控制和监测中准确高效的使用激光雷达测风数据,需要对激光雷达测风数据进行高效、快速、准确的实时处理。目前机舱式激光雷达测风面临如下问题：激光雷达在受到来流风时会跟随机舱振动,导致实测数据波动进而对风速重构算法产生影响;激光雷达会受到风电机组叶片遮挡,导致实测数据缺失或出现无效数据。本文通过合理配置激光雷达参数,以雷达测风数据作为研究对象,对测风数据进行数值修正,消除机舱振动带来的误差,开发出一套先进先出嵌套循环判断填充算法解决叶片遮挡问题,建立线性剪切风场模型,基于递推最小二乘法求解风场特征参数,最后通过泰勒冻结湍流假说计算风轮面转子有效风速,与机舱内控制参数反演出的转子有效风速进行对比,得出两组数据相关性在0.9374,两组数据差值的标准差为0.3429,结果证明在实际应用中,使用该配置参数的激光雷达通过坐标修正和数据填充等技术手段,开发的风速重构模型算法能够准确的为风电机组控制系统提供可靠的控制输入参数。     

毫米波雷达与激光雷达在无人船上的应用

从毫米波雷达与激光雷达在无人驾驶汽车及水面自主船舶的应用现状出发,分析其相对于船舶现有感知设备的优缺点。提出基的毫米波雷达与激光雷达在无人船上近距离目标运动态势融合感知方案,并给出具体的数据处理与融合过程。该种组合感知方案能够有效弥补现有无人船感知盲区、提高近距离目标感知精度及自主靠离泊的可靠性。最后指出激光雷达在未来需要降低成本并提高海事可靠性,与视觉传感器的融合将会是更高层次上的研究方向。     

Autonomous vehicle self-localization based on abstract map and multi-channel LiDAR in urban area

Accurate vehicle self-localization is significant for autonomous driving. The localization techniques based on Global Navigation Satellite System (GNSS) cannot achieve the required accuracy in urban canyons. On the other hand, simultaneous localization and mapping (SLAM) methods suffer from the error accumulation problem. State-of-the-art localization approaches adopt 3D Light Detection and Ranging (Lidar) to observe the surrounding environment and match the observation with a priori known 3D point cloud map for estimating the position of the vehicle within the map. However, storing the massive point cloud needs immense storage on the vehicle, or it should be stored on servers, which makes the simultaneous downloading of the map by multiple vehicles another challenge. In this study, rather than employing the point cloud directly as the prior map, we focus on the abstract map of buildings, which is easy to extract, and at the same time apparently observable by Lidar. More especially, we proposed vehicle localization methods based on two different abstract map formats representing urban areas. The first format is the multilayer 2D vector map of building footprints, which represents the building boundaries using vectors (lines). The second format is the planar surface map of buildings and ground. These map formats share the same idea that the uncertainty (deviation) of each vector or planar surface is calculated and included in the map. Later in the localization phase, the observed data from Lidar is matched with the abstract map to obtain the precise location of the vehicle. Experiments conducted in a dense urban area of Tokyo show that even though we significantly shrank the map size, we could preserve the mean error of the localization.