Perception is a key element for enabling intelligent autonomous navigation. Understanding the semantics of the surrounding environment and accurate vehicle pose estimation are essential capabilities for autonomous vehicles, including self-driving cars and mobile robots that perform complex tasks. Fast moving platforms like self-driving cars impose a hard challenge for localization and mapping algorithms. In this work, we propose a novel framework for real-time LiDAR odometry and mapping based on LOAM architecture for fast moving platforms. Our framework utilizes semantic information produced by a deep learning model to improve point-to-line and point-to-plane matching between LiDAR scans and build a semantic map of the environment, leading to more accurate motion estimation using LiDAR data. We observe that including semantic information in the matching process introduces a new type of outlier matches to the process, where matching occur between different objects of the same semantic class. To this end, we propose a novel algorithm that explicitly identifies and discards potential outliers in the matching process. In our experiments, we study the effect of improving the matching process on the robustness of LiDAR odometry against high speed motion. Our experimental evaluations on KITTI dataset demonstrate that utilizing semantic information and rejecting outliers significantly enhance the robustness of LiDAR odometry and mapping when there are large gaps between scan acquisition poses, which is typical for fast moving platforms.

翻译：感知是实现智能自主导航的关键要素。理解周围环境的语义信息以及精确的车辆位姿估计是自主汽车（包括执行复杂任务的自动驾驶汽车和移动机器人）的基本能力。自动驾驶汽车等快速移动平台对定位与建图算法提出了严峻挑战。本文提出了一种基于LOAM架构的快速移动平台实时激光雷达里程计与建图新框架。该框架利用深度学习模型生成的语义信息，改进了激光雷达扫描间的点-线和点-面匹配，并构建环境语义地图，从而利用激光雷达数据实现了更精确的运动估计。我们发现在匹配过程中引入语义信息会带来一种新型的异常匹配——即同一语义类别的不同对象之间发生匹配。为此，我们提出了一种新颖算法，可在匹配过程中明确识别并排除潜在的异常匹配。在实验中，我们研究了改进匹配过程对激光雷达里程计抗高速运动鲁棒性的影响。在KITTI数据集上的实验评估表明，当扫描采集位姿之间存在较大间隙（这在快速移动平台中很常见）时，利用语义信息和排除异常匹配能显著增强激光雷达里程计与建图的鲁棒性。