  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.
