This work addresses the problem of mapping terrain features based on inertial and LiDAR measurements to estimate navigation cost, for an autonomous ground robot. The navigation cost quantifies the degree of how easy or difficult it is to navigate through different areas. Unlike most indoor applications, where surfaces are usually human-made, flat, and structured, external environments may be unpredictable as to the types and conditions of the travel surfaces, such as traction characteristics and inclination. Attaining full autonomy in outdoor environments requires a mobile ground robot to perform the fundamental localization and mapping tasks in unfamiliar environments, but with the added challenge of unknown terrain conditions. Autonomous motion in uneven terrain has been widely explored by the research community focusing on one or more of the several factors involved aiming at both safety and efficient displacement. A fuller representation of the environment is fundamental to increase confidence and to reduce navigation costs. To this end we propose a methodology composed of five main steps: (i) speed-invariant inertial transformation; (ii) roughness level classification; (iii) navigation cost estimation; (iv) sensor fusion through Deep Learning; and (v) estimation of navigation costs for untraveled regions. To validate the methodology, we carried out experiments using ground robots in different outdoor environments with different terrain characteristics. Results show that the inertial data transformation reduces the dispersion of signal magnitude for different speeds and scenarios. Meanwhile, the roughness level classification achieved a mean accuracy of 95.4%, for the speed of 0.6 m/s. Finally, the obtained terrain maps are a faithful representation of outdoor environments allowing accurate and reliable path planning.

这项工作针对自主地面机器人，解决了基于惯性和LiDAR测量绘制地形特征以估计导航成本的问题。导航成本量化了在不同区域中导航的难易程度。与大多数室内应用程序不同，在大多数情况下，表面通常是人造的，平坦的和结构化的，外部环境可能无法预测行驶表面的类型和状况，例如牵引特性和倾斜度。要在室外环境中获得完全自主权，需要移动地面机器人在不熟悉的环境中执行基本的定位和制图任务，但还要增加未知地形条件的挑战。研究界已经广泛地研究了不平坦地形中的自主运动，其重点是针对安全性和有效位移的几个因素中的一个或多个。完整地表示环境对于提高信心和降低导航成本至关重要。为此，我们提出了一种由五个主要步骤组成的方法：（i）速度不变惯性变换；（ii）粗糙度等级分类；（iii）导航费用估算；（iv）通过深度学习进行传感器融合；（v）估算未旅行区域的航行费用。为了验证该方法，我们在具有不同地形特征的不同室外环境中使用地面机器人进行了实验。结果表明，惯性数据变换降低了不同速度和场景下信号幅度的离散。同时，粗糙度等级分类的平均精度为95.4％，速度为0.6米/秒。最后，获得的地形图是室外环境的忠实代表，可以进行准确而可靠的路径规划。