We present a terrain traversability mapping and navigation system (TNS) for autonomous excavator applications in an unstructured environment. We use an efficient approach to extract terrain features from RGB images and 3D point clouds and incorporate them into a global map for planning and navigation. Our system can adapt to changing environments and update the terrain information in real-time. Moreover, we present a novel dataset, the Complex Worksite Terrain (CWT) dataset, which consists of RGB images from construction sites with seven categories based on navigability. Our novel algorithms improve the mapping accuracy over previous SOTA methods by 4.17-30.48% and reduce MSE on the traversability map by 13.8-71.4%. We have combined our mapping approach with planning and control modules in an autonomous excavator navigation system and observe 49.3% improvement in the overall success rate. Based on TNS, we demonstrate the first autonomous excavator that can navigate through unstructured environments consisting of deep pits, steep hills, rock piles, and other complex terrain features.
翻译:我们用一种高效的方法从RGB图像和3D点云中提取地形特征,并将其纳入规划和导航的全球地图;我们的系统可以适应不断变化的环境,实时更新地形信息;此外,我们用一套新颖的数据集,即复杂工作地地形数据集,由建筑地点基于导航能力的七类建筑地点的RGB图像组成;我们的新算法比以前SOTA方法提高了4.17-30.48%的绘图准确性,将移动性地图上的MSE减少了13.8-71.4 %;我们把绘图方法与自主挖掘机导航系统的规划和控制模块结合起来,并观察总体成功率的49.3%的改善情况;在TNS的基础上,我们展示了第一个能够通过由深坑、陡峭山、岩堆和其他复杂地形特征组成的无结构环境进行导航的自主挖掘器。