Odometry with lidar sensors is a state-of-the-art method to estimate the ego pose of a moving vehicle. Many implementations of lidar odometry use variants of the Iterative Closest Point (ICP) algorithm. Real-world effects such as dynamic objects, non-overlapping areas, and sensor noise diminish the accuracy of ICP. We build on a recently proposed method that makes these effects visible by visualizing the multidimensional objective function of ICP in two dimensions. We use this method to study different ICP variants in the context of lidar odometry. In addition, we propose a novel method to filter out dynamic objects and to address the ego blind spot problem.

翻译：基于激光雷达传感器的里程计是估计移动车辆自身位姿的先进方法。许多激光雷达里程计的实现采用了迭代最近点（ICP）算法的不同变体。现实世界中的动态物体、非重叠区域以及传感器噪声等因素会降低ICP的精度。我们基于最近提出的一种方法展开研究，该方法通过将ICP的多维目标函数可视化至二维空间，使这些影响因素得以显现。我们运用此方法，在激光雷达里程计的背景下研究不同的ICP变体。此外，我们提出了一种新颖的方法来滤除动态物体，并解决自身盲点问题。