Vi utbildar för framtiden och skapar samhällsnytta genom vår forskning som levandegörs i nära samarbete med näringslivet. Vi bedriver forskning inom computer science, datateknik, software engineering och interaktionsdesign - från grundforskning till direkta tillämpningar. Institutionen har en stark internationell prägel och är delad mellan Chalmers och Göteborgs universitet.
We are engaged in research and education across the full spectrum of computer science, computer engineering, software engineering, and interaction design, from foundations to applications. We educate for the future, conduct research with high international visibility, and create societal benefits through close cooperation with businesses and industry. The department is joint between Chalmers and the University of Gothenburg.
(2023) Wang, Zilong; Chang, Qi; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Heyn, Hans-Martin; Cabrero-Daniel, Beatriz
Ground surface monitoring using 3D visualization techniques has gained significant attention in recent years, particularly in the context of autonomous driving.
This research presents an implementation of a cost-efficient 3D visualization solution for soil surface analysis, aiming to explore an alternative approach to Light Detection and Ranging (LiDAR) technology. The research investigates the feasibility of utilizing stereo cameras as an affordable option for generating 3D visualization. A comparative study between various stereo vision 3D reconstruction methods is conducted to evaluate their performance.
Different stereo-matching methods are employed to extract depth information from the captured stereo images, including a machine learning method and a traditional semi-global block matching method. The resulting depth maps are then projected onto a 3D space, enabling the generation of point cloud data for visualization purposes. The visualized 3D representation provides an enhanced understanding of the soil surface conditions and facilitates detailed analysis.
To assess the effectiveness of the implemented visualization solution, several metrics are employed as a measure to compare the accuracy of the generated visualizations. Time measurement and Root Mean Square Error (RMSE) analysis serve as benchmarks for evaluating the performance and reliability of the proposed 3D visualization approach.
The findings of this research demonstrate the potential of stereo cameras as a costeffective alternative to LiDAR sensors for soil surface analysis. The presented 3D visualization solution may contribute to autonomous construction vehicles by providing an efficient and affordable function for monitoring soil surface conditions.