Abstract:

At present, the data fusion of the global navigation satellite system and the light detection and ranging is widely used in the positioning system of autonomous vehicles, but in the indoor environment, the loss of satellite signals leads to low positioning accuracy or even positioning failure. Therefore, a fusion positioning algorithm based on Ultra-Wideband（UWB） and Light Detection and Ranging（LiDAR） is proposed. The algorithm is based on particle filter to solve the location data of two sensors by complementary fusion. The real-time positioning data of UWB is used to improve the positioning speed of LiDAR by providing the range of starting particles. The weight of particles is updated by solving the geometric distance between the LiDAR positioning information and the particles, thereby compensating the positioning error of UWB in a non-line-of-sight environment. An indoor test scene is built, and the fusion positioning algorithm is verified on the smart car platform. The experimental results show that this method is superior to the single sensor positioning scheme of UWB or LiDAR, and the vehicle can still obtain good positioning accuracy and real-time performance when the UWB line of sight is blocked or LiDAR matching fails.

Key words: indoor positioning, ultra-wideband, Light Detection and Ranging（LiDAR）, data fusion, particle filter

摘要：

当前全球导航卫星系统与激光雷达的数据融合被广泛应用于无人驾驶车辆的定位系统中，但在室内环境下由于卫星信号的丢失导致定位精度低甚至无法定位。为此提出一种基于超宽带（Ultra-Wideband，UWB）与激光雷达（Light Detection and Ranging，LiDAR）的融合定位算法。该算法以粒子滤波为基础，对两个传感器的定位数据进行互补融合解算。利用UWB实时定位数据通过提供起始粒子范围的方式来提高LiDAR的定位速率。通过求解LiDAR定位信息与粒子之间的几何距离来更新粒子的权重，从而弥补UWB的非视距误差。搭建一个室内测试场景，并将融合定位算法在智能小车平台上进行验证。实验结果表明，该方法优于UWB或LiDAR单一传感器的定位方案，而且在UWB视距受阻或LiDAR匹配失效的情况下，车辆仍能够获得良好的定位精度和定位实时性。

关键词: 室内定位, 超宽带, 激光雷达, 数据融合, 粒子滤波