论文标题
增强的激光扫描匹配，以及在线错误估计高速公路和隧道驾驶
Enhanced Laser-Scan Matching with Online Error Estimation for Highway and Tunnel Driving
论文作者
论文摘要
LIDAR数据可用于生成点云，用于导航自动驾驶汽车或移动机器人平台。扫描匹配是估算最能使两个点云的刚性转换的过程，是LiDAR探射仪的基础，这是一种死亡算法的形式。当无法使用GPS（例如GPS）时，LIDAR射量特别有用。在这里，我们提出了迭代最接近的椭圆形变换（ICET），这是一种扫描匹配算法，对当前最新的正常分布变换（NDT）提供了两种新颖的改进。像NDT一样，ICET将激光雷达数据分解为体素，并将高斯分布拟合到每个体素内的点。 ICET的第一次创新通过沿着这些方向抑制溶液来降低沿着大型平坦表面的几何歧义。 ICET的第二个创新是推断与连续点云之间的位置和方向转换相关的输出误差协方差；当将ICET纳入诸如扩展的Kalman滤波器之类的状态估算程序中时，误差协方差特别有用。我们构建了一个模拟，以比较有或没有几何歧义的2D空间中ICET和NDT的性能，并发现ICET产生了出色的估计值，同时可以准确预测溶液的准确性。
Lidar data can be used to generate point clouds for the navigation of autonomous vehicles or mobile robotics platforms. Scan matching, the process of estimating the rigid transformation that best aligns two point clouds, is the basis for lidar odometry, a form of dead reckoning. Lidar odometry is particularly useful when absolute sensors, like GPS, are not available. Here we propose the Iterative Closest Ellipsoidal Transform (ICET), a scan matching algorithm which provides two novel improvements over the current state-of-the-art Normal Distributions Transform (NDT). Like NDT, ICET decomposes lidar data into voxels and fits a Gaussian distribution to the points within each voxel. The first innovation of ICET reduces geometric ambiguity along large flat surfaces by suppressing the solution along those directions. The second innovation of ICET is to infer the output error covariance associated with the position and orientation transformation between successive point clouds; the error covariance is particularly useful when ICET is incorporated into a state-estimation routine such as an extended Kalman filter. We constructed a simulation to compare the performance of ICET and NDT in 2D space both with and without geometric ambiguity and found that ICET produces superior estimates while accurately predicting solution accuracy.
