Automated maintenance and motion planning for unpaved roads are research areas of great interest in the field robotics. Constructing such systems necessitates the development of surface maps for unpaved roads. However, the lack of distinctive features on unpaved roads degrades the performance of light detection and ranging (LiDAR)‐based mapping. To address this problem, this paper proposes three‐dimensionalized feature‐based LiDAR‐visual odometry (TFB odometry) for the online mapping of unpaved road surfaces. TFB odometry introduces a novel interpolation concept to directly estimate the three‐dimensional coordinates of the image features using LiDAR. Furthermore, LiDAR intensity‐weighted motion estimation is proposed to effectively mitigate the effects of dust, which significantly impact the performance of LiDAR. Finally, TFB odometry includes pose graph optimization to efficiently fuse global navigation satellite system data and poses estimated from motion estimation. Through field experiments on unpaved roads, TFB odometry demonstrated successful online full mapping and outperformed other simultaneous localization and mapping methods. Additionally, it demonstrated remarkable performance in accurately mapping road surface anomalies, even in dusty regions.

中文翻译：

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基于三维特征的激光雷达视觉里程计用于未铺砌路面的在线测绘

未铺砌道路的自动维护和运动规划是现场机器人技术非常感兴趣的研究领域。构建此类系统需要开发未铺砌道路的路面地图。然而，未铺砌的道路上缺乏明显的特征会降低基于光检测和测距（LiDAR）的测绘的性能。为了解决这个问题，本文提出了基于三维特征的激光雷达视觉里程计（TFB里程计），用于未铺砌路面的在线测绘。 TFB 里程计引入了一种新颖的插值概念，可以使用 LiDAR 直接估计图像特征的三维坐标。此外，提出了激光雷达强度​​加权运动估计，以有效减轻灰尘的影响，灰尘对激光雷达的性能有显着影响。最后，TFB 里程计包括位姿图优化，以有效融合全球导航卫星系统数据和根据运动估计估计的位姿。通过在未铺砌道路上的现场实验，TFB 里程计展示了成功的在线全地图绘制，并且优于其他同步定位和地图绘制方法。此外，即使在尘土飞扬的地区，它在准确绘制路面异常情况方面也表现出了卓越的性能。