SLAM-CENTRIC FRAMEWORK FOR PRECISE AND PLATFORM-AGNOSTIC ROBOT-AIDED INFRASTRUCTURE INSPECTION

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Robot-aided inspection has emerged as a promising solution for enhancing safety, efficiency, and objectivity in infrastructure assessment. However, existing approaches often suffer from inconsistent mapping accuracy, unreliable defect measurements, and platform-specific system designs that limit scalability. This study investigates whether a SLAM-centric (Simultaneous Localization and Mapping) framework can overcome these limitations and enable precise, repeatable, and platform-agnostic visual inspections across diverse infrastructure environments.

Integrated Lidar–Camera–Inertial SLAM Architecture

The proposed framework integrates lidar, camera, and inertial measurement unit (IMU) data within a unified SLAM pipeline to ensure robust localization and mapping under real-world conditions. Multi-sensor fusion enhances pose estimation accuracy and resilience to environmental challenges such as lighting variation, occlusions, and geometric complexity. By centering the inspection workflow around high-fidelity SLAM, the system establishes a reliable spatial reference for defect mapping and measurement, independent of the robotic platform employed.

Offline Trajectory Refinement and Inspection Map Generation

To further improve mapping precision, the framework incorporates offline trajectory refinement, reducing drift and cumulative localization errors commonly observed in real-time SLAM systems. Refined trajectories enable the generation of dense and geometrically consistent inspection maps. These maps serve as a unified spatial representation where inspection data can be consistently overlaid, facilitating repeatable assessments and longitudinal monitoring of infrastructure assets.

Automated Defect Extraction and 3D Ray-Tracing Projection

Visual defect detection is performed through image-based analysis, extracting cracks, spalls, and surface anomalies from captured imagery. A 3D ray-tracing technique projects detected defects into the unified inspection map, ensuring accurate spatial localization and dimensional quantification. This method allows precise measurement of defect size, orientation, and position within the 3D structure, significantly improving reliability compared to traditional qualitative or manual inspection methods.

Validation in Real-World Scenarios

Experimental validation in real-world environments demonstrates that the SLAM-centric framework produces accurate defect localization, consistent dimensional measurements, and high-density inspection maps. The platform-agnostic design ensures adaptability across different robotic systems, including ground vehicles, aerial drones, and climbing robots. The repeatability and robustness of the approach confirm its suitability for practical infrastructure inspection applications.

Implications for Long-Term Monitoring and Automation

By providing an end-to-end solution for robot-aided inspection, the proposed framework enables faster, safer, and more objective infrastructure assessments. The release of datasets and open software tools establishes a foundation for future research in long-term defect monitoring, inspection automation, and predictive maintenance. This SLAM-centric paradigm represents a significant step toward intelligent, data-driven infrastructure management systems.

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#InfrastructureSafety
#AIforEngineering
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