Vehicle distance measurement and calibration method based on laser radar and binocular vision fusion

By fusing lidar and binocular vision, the system adaptively identifies road surface reflection and triggers a self-calibration mechanism, solving the stability and accuracy problems of vehicle distance measurement under wet, slippery, or waterlogged road conditions. This enables real-time anomaly detection and adaptive correction under complex road conditions.

CN122017810APending Publication Date: 2026-05-12GANSU PROVINCIAL INST OF METROLOGY
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Patent Information

Application Number
CN202610285270.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-10
Publication Date
2026-05-12

AI Technical Summary

Technical Problem

Under conditions of rain, snow, slippery roads, or water accumulation, the existing highway vehicle distance capture system's lidar and binocular vision measurement methods are easily affected by road surface reflections, leading to systematic shifts or fluctuations in vehicle distance calibration results, which reduces the stability and accuracy of distance measurement.

Method used

By using a fusion method of lidar and binocular vision, the system adaptively identifies the road surface reflection status and the differences in multi-source ranging stability, constructs a specular reflection status code, triggers a self-calibration mechanism, and determines the dominant source of ranging anomalies through symbol analysis, selecting either offset calibration or fusion correction strategies to form a closed-loop processing flow.

Benefits of technology

Real-time anomaly detection and adaptive correction were achieved under complex road conditions, improving the reliability and stability of vehicle distance measurement and reducing distance measurement error.

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Abstract

The invention discloses a vehicle distance measurement and calibration method based on laser radar and binocular vision fusion, relates to the technical field of measurement and calibration, and is used for solving the problem that the precision of a vehicle distance calibration result is reduced when a laser radar used for calibrating a highway vehicle distance snapshot system is under the conditions of rain, snow, wet and slippery or waterlogging road surfaces. Joint modeling is carried out on spatial distribution and echo energy characteristics of laser radar low-height point cloud, a point cloud echo coefficient and a low-height number proportion are introduced, a specular reflection state code is constructed, a self-calibration mechanism is adaptively triggered accordingly, and a specular reflection state is obtained. The discrete characteristic of a laser radar vehicle distance time sequence and the degradation trend of binocular vision parallax connectivity are subjected to quantitative analysis, the dominant source of ranging abnormity is judged through symbolic analysis, and an offset calibration or fusion correction strategy is selected according to the dominant source. Reliability and engineering adaptability of vehicle distance measurement under the wet and slippery road surface condition can be improved under the condition of not depending on an additional sensor.
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