TNES: terrain traversability mapping, navigation and excavation system for autonomous excavators on worksite

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 navigatio...

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Bibliographic Details
Published in:Autonomous robots 2023-08, Vol.47 (6), p.695-714
Main Authors: Guan, Tianrui, He, Zhenpeng, Song, Ruitao, Zhang, Liangjun
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Autonomous navigation
Changing environments
Color imagery
Computer Imaging
Construction sites
Control
Datasets
Engineering
Excavation
Excavators
Mapping
Mechatronics
Navigation systems
Pattern Recognition and Graphics
Robotics
Robotics and Automation
Terrain
Three dimensional models
Vision
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Summary: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 dataset, which consists of RGB images from construction sites with seven categories based on navigability. Our novel algorithms improve the mapping accuracy over previous 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. In addition, we combine the proposed TNS with the autonomous excavation system (AES), and deploy the new pipeline, TNES, on a more complex construction site. With minimum human intervention, we demonstrate autonomous navigation capability with excavation tasks.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-023-10113-9