Abstract: Objective: To guide metro tunnel maintenance and achieve intelligent detection of shield tunnel staggering, research is conducted on metro shield tunnel staggering intelligent detection method based on laser point cloud technology（abbreviated as LPCT）. Method: The detection principles of LPCT are introduced. Point cloud data processing technology is briefly described from aspects including tunnel profile extraction and fitting noise reduction techniques, grayscale and depth map automatic generation techniques, and automatic analysis of staggering defects. The detection process using LPCT is outlined. Taking a metro line in Wuhan as example, the onsite detection of shield tunnel segment lining surface staggering is conducted using a mobile threedimensional laser scanning system and LPCT. The repetitive accuracy of LPCT is verified using staggering data from the same tunnel and the same equipment but at different time periods, while the relative accuracy of LPCT is verified using staggering data from the same tunnel and different equipment but at the same time period. Result and Conclusion: The average deviation of staggering data output from the same tunnel and the same equipment at different time periods is 0.357 5 mm, while the above average deviation at the same time period is 0.360 0 mm. This validates the applicability and superiority of LPCT in tunnel staggering intelligent detection.