
Research on the Application of Industrial Robot Technology in Large-Scale Geodetic Data Acquisition
DOI:
https://doi.org/10.30564/jees.v8i2.13075Abstract
Large-scale geodetic data acquisition is fundamental to infrastructure lifecycle management, construction quality control, urban digital twins, and hazard monitoring, yet conventional surveying workflows remain labor-intensive and difficult to scale in complex or hazardous environments. The industrial robot technology is proving to be an enabling technology in providing repeatable, high-throughput, and safety-conscious geodetic acquisition through its ability to offer controllable motion, stable sensor deployment, and autonomy coupled with perception stacks. The review itself is a synthesis of the recent studies on robot-based geodetic acquisition from the platform workflow application perspective. We summarize in the priority industrial robot platforms which have potential applications in geodesy, distinction being made between those based on autonomous mobile robots, mobile manipulators, fixed-base manipulators, cooperative multi-robot arrangements, and the design considerations underlying their construction: geometric stability, payload loading, and tightly constrained safety of operation. We then consider sensing configurations, principles of calibration and synchronization, as well as acquisition strategies that regulate the completeness of data and measurement consistency. The foundations of core processing are examined in light of georeferencing, registration, Simultaneous Localization and Mapping (SLAM)-based localization, and uncertainty propagation, which are essential to achieve survey-grade outputs. The evidence of application is discussed in the framework of infrastructure monitoring, construction, industrial facilities, urban/corridor mapping, mining, and indoor/underground settings, showing areas of obvious robotics advantage in repeatability and risk mitigation, as well as conditions of limitation because of the Global Navigation Satellite System (GNSS) denial, drift, calibration sensitivity, and inconsistent evaluation practices. Lastly, we determine research priorities such as benchmark datasets and metrics, accuracy-motivated autonomy, strong multi-sensor fusion with uncertainty results, and a closer association with Building Information Modeling (BIM)/digital twin pipelines.
Keywords:
Industrial Robots; Geodetic Data Acquisition; Mobile Mapping; SLAM; Sensor FusionReferences
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Guangxiang Zhang