A Mobile Robotic 3-D Measurement Method Based on Point Clouds Alignment for Large-Scale Complex Surfaces

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Abstract
Large-scale complex surfaces are the core components of large aircraft, ships, and wind turbine generators. High-accuracy manufacturing of the components is very crucial for its performance. Automated and high-accuracy large-scale 3-D measurement is an effective means for the manufacturing accuracy control of these components. This article proposes an automated mobile robotic profile measurement method for large-scale complex surfaces, and it also proposes a high-accuracy point clouds alignment algorithm to improve the measurement accuracy. A fringe projection scanner, integrated on the end of a mobile robot, is utilized to scan the surface and obtain the local scanning point clouds. The local scanning point clouds' coarse alignment is implemented using the robot's joint data and the laser sensor's positioning data. After that, a fine alignment algorithm combining the fruit fly optimization algorithm (FOA) with the improved iterative closest point (IICP) algorithm is proposed for point clouds' fine alignment. Practical measurement was performed on a sectional wind turbine blade, and a broad region of 4.6 m ×2.6 m was measured. An accuracy evaluation method based on point-to-plane distance was adopted to evaluate the measurement error. The result that the mean distance error is less than 0.4 mm shows the proposed mobile robotic measurement system and the aligning method are of high accuracy.
Funding Information
  • National Natural Science Foundation of China (91948301, 91748204)
  • State Key Laboratory of Robotics and System (SKLRS-2019-KF-11)
  • Major Project of Hubei Province Technology Innovation (2019AAA071)

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