论文标题
高精度室内定位
High precision indoor positioning by means of LiDAR
论文作者
论文摘要
自主驾驶和汽车区域持续研究的趋势，例如先进的驾驶员援助系统（ADAS），都需要在任何情况下进行准确的本地化。对车辆状态的准确估计是任何轨迹规划算法的基本要求。尽管如此，即使引入GPS L5频段也承诺要涉及车道准确性，仍必须解决屋顶区域的覆盖范围限制。在这项工作中，提出了一种使用LIDAR高精度室内定位的方法。该方法基于运动模型与激光雷达测量的组合，并将基础设施元素用作定位参考。这允许估计在局部切线平面（LTP）参考框架中车辆的方向，速度和位置。当将提出的方法的输出与汽车动态运动分析仪（ADMA）的输出相提并论时，平均误差为1度，分别获得0.1 m/s和4.7 cm的误差。该方法可以通过使用激光雷达传感器作为独立单元来实现。在Intel I7-6820HQ CPU上，我们在40.77 US的中位运行时间标志着实时处理的可能性。
The trend towards autonomous driving and the continuous research in the automotive area, like Advanced Driver Assistance Systems (ADAS), requires an accurate localization under all circumstances. An accurate estimation of the vehicle state is a basic requirement for any trajectory-planning algorithm. Still, even when the introduction of the GPS L5 band promises lane-accuracy, coverage limitations in roofed areas still have to be addressed. In this work, a method for high precision indoor positioning using a LiDAR is presented. The method is based on the combination of motion models with LiDAR measurements, and uses infrastructural elements as positioning references. This allows to estimate the orientation, velocity over ground and position of a vehicle in a Local Tangent Plane (LTP) reference frame. When the outputs of the proposed method are compared to those of an Automotive Dynamic Motion Analyzer (ADMA), mean errors of 1 degree, 0.1 m/s and of 4.7 cm respectively are obtained. The method can be implemented by using a LiDAR sensor as a stand-alone unit. A median runtime of 40.77 us on an Intel i7-6820HQ CPU signals the possibility of real-time processing.
