Gamification for road asset inspection from Mobile Mapping System data

ABSTRACT

Gamification techniques have been proven effective in various fields such as education and industry. In this paper, we introduce a novel approach that applies gamification techniques to the identification of road assets in Mobile Laser Scanning (MLS) data. Our method utilises three gamification techniques: avatar (vehicle), point cloud segmentation into levels, and scoring. We implemented these techniques in Unreal Engine and evaluated their performance using three real-world case studies. We also compared two ways of point cloud visualisation: mesh-based and point-based. Our results demonstrate that our gamification approach improves the handling and visualisation of point clouds when compared to other free software such as Cloud Compare. Specifically, the point-based visualisation method provides a more accurate representation of the road environment and the input point cloud and is easier to import into Unreal Engine. However, this method requires more computational resources for visualisation. On the other hand, level segmentation ensures a constant frame rate of 60 frames per second. Furthermore, our gamification approach enhances the experience of road asset identification, making it more enjoyable for the user. However, we acknowledge that the quality of the point cloud remains the primary factor affecting the accuracy of asset identification, regardless of the software used. Overall, our proposed gamification approach offers a promising solution for improving the identification of road assets in MLS data and has the potential to be applied to other fields beyond road asset identification.

KEYWORDS:

• LiDAR
• Unreal Engine
• game engines
• point clouds
• Mobile Laser Scanning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research has received funding from Xunta de Galicia through human resources grant [ED481B-2019-061], the Ministerio de Ciencia, Innovación y Universidades -Gobierno de España-, grant number [PID2019-105221RBC43/AEI/10.13039/50110001 033, and RYC2021‐‐033560‐‐I], and RYC2021‐033560‐I funded by [MCIN/AEI/ 10.13039/501100011033] and by European Union NextGenerationEU/PRTR. This paper was carried out in the framework of the InfraROB project (Maintaining integrity, performance, and safety of the road infrastructure through autonomous robotized solutions and modularization), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement [no. 955337]. It reflects only the authors’ views. Neither the European Climate, Infrastructure, and Environment Executive Agency (CINEA) nor the European Commission is in any way responsible for any use that may be made of the information it contain.