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We present a terrain traversability mapping and navigation system (TNS) forautonomous excavator applications in an unstructured environment. We use anefficient approach to extract terrain features from RGB images and 3D pointclouds and incorporate them into a global map for planning and navigation. Oursystem can adapt to changing environments and update the terrain information inreal-time. Moreover, we present a novel dataset, the Complex Worksite Terrain(CWT) dataset, which consists of RGB images from construction sites with sevencategories based on navigability. Our novel algorithms improve the mappingaccuracy over previous SOTA methods by 4.17-30.48% and reduce MSE on thetraversability map by 13.8-71.4%. We have combined our mapping approach withplanning and control modules in an autonomous excavator navigation system andobserve 49.3% improvement in the overall success rate. Based on TNS, wedemonstrate the first autonomous excavator that can navigate throughunstructured environments consisting of deep pits, steep hills, rock piles, andother complex terrain features.

TNS: Terrain Traversability Mapping and Navigation System for Autonomous Excavators