Wheel alignment inspection by 3D point cloud monitoring

다국어 초록 (Multilingual Abstract)

Today’s wheel alignment inspection systems adopt various computer vision technologies. They, however, require high-end cameras,precisely manufactured targets, and massive calculation loops because they rely on low-dimensional data (two-dimensional images) formeasuring higher-dimensional information (three-dimensional orientation) of the wheel posture. To improve this, a simple and inexpensivemethod using a consumer-grade depth-sensing camera such as Kinect is presented. It directly utilizes point clouds generated from itsrange image stream. All points within the region of interest (ROI) contain geometrical information of the wheel and are used for thealignment inspection procedures. Its feasibility is evaluated by examining whether the orientation could be aligned to the desired orientationusing only the point cloud data. For verification, a one-wheel-based prototype was implemented, and comparative experiments withan existing commercial system were conducted. The experimental results showed that the proposed method provides satisfactory performance.

We believe that the proposed method is feasible for practical usage and has a great potential to be an effective alternative toexisting wheel alignment inspection methods.

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