Development of Integrative Methodologies for Effective Excavation Progress Monitoring
| Autor: | Jaho Seo, Abdullah Rasul, Amir Khajepour |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
LiDAR
Computer science 0211 other engineering and technologies bucket volume estimation convolutional neural network 5D mapping 02 engineering and technology computer.software_genre lcsh:Chemical technology Biochemistry Article Analytical Chemistry Component (UML) stereo vision camera 021105 building & construction 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation ground volume estimation business.industry Volume (computing) excavation progress Excavation Automation Atomic and Molecular Physics and Optics occlusion area Excavator Lidar proprioceptive and exteroceptive sensors 020201 artificial intelligence & image processing Data mining business computer |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 21 Issue 2 Sensors, Vol 21, Iss 364, p 364 (2021) |
| ISSN: | 1424-8220 |
| Popis: | Excavation is one of the primary projects in the construction industry. Introducing various technologies for full automation of the excavation can be a solution to improve sensing and productivity that are the ongoing issues in this area. This paper covers three aspects of effective excavation progress monitoring that include excavation volume estimation, occlusion area detection, and 5D mapping. The excavation volume estimation component enables estimating the bucket volume and ground excavation volume. To achieve mapping of the hidden or occluded ground areas, integration of proprioceptive and exteroceptive sensing data was adopted. Finally, we proposed the idea of 5D mapping that provides the info of the excavated ground in terms of geometric space and material type/properties using a 3D ground map with LiDAR intensity and a ground resistive index. Through experimental validations with a mini excavator, the accuracy of the two different volume estimation methods was compared. Finally, a reconstructed map for occlusion areas and a 5D map were created using the bucket tip&rsquo s trajectory and multiple sensory data with convolutional neural network techniques, respectively. The created 5D map would allow for the provision of extended ground information beyond a normal 3D ground map, which is indispensable to progress monitoring and control of autonomous excavation. |
| Databáze: | OpenAIRE |
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