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An Improved Calibration Method for Photonic Mixer Device Solid-State Array Lidars Based on Electrical Analog Delay.

Xuanquan Wang¹, Ping Song¹, Wuyang Zhang¹

¹Key Laboratory of Biomimetic Robots and Systems (Ministry of Education), Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)

|January 9, 2021

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This study introduces an improved calibration method for photonic mixer device (PMD) Lidar, enhancing depth accuracy. The new technique effectively compensates for ambient light and target reflectivity variations, improving Lidar performance.

Area of Science:

Optoelectronics and Photonics
Robotics and Autonomous Systems
Computer Vision

Background:

Photonic Mixer Device (PMD) Lidar, utilizing indirect time-of-flight (ToF) technology, offers high resolution and accuracy for applications like SLAM and industrial inspection.
Conventional PMD Lidar calibration methods are inefficient and inaccurate due to the need for large-area distance adjustments.
Environmental factors (ambient light, temperature) and target characteristics (reflectivity) significantly impact PMD Lidar performance.

Purpose of the Study:

To develop an improved, efficient, and accurate calibration method for PMD Lidar systems.
To address the limitations of conventional calibration techniques by introducing a novel approach.
To enhance the ranging accuracy and adaptability of PMD Lidar across diverse scenarios.

Keywords:

PMD solid-state array Lidar depth calibration method electrical analog delay joint calibration algorithm photonic mixer device self-adaptive grayscale correlation

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Last Updated: Nov 22, 2025

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Main Methods:

An improved calibration method based on electrical analog delay is proposed.
Lens distortion is corrected using a self-adaptive interpolation algorithm.
Grayscale image calibration is performed via an integral time simulation, and ambient light compensation parameters are estimated for depth calibration.

Main Results:

The proposed method effectively compensates for lens distortion and calibrates grayscale images.
Ambient light compensation is integrated into depth calibration using grayscale image estimations.
Combining four compensation types significantly enhances depth calibration performance.

Conclusions:

The developed calibration method demonstrates superior adaptability to various scenes and target reflectivities.
The technique significantly improves the ranging accuracy and overall adaptability of PMD Lidar.
This advancement contributes to more reliable and precise Lidar applications in demanding environments.