An improved calibration method for photonic mixer device solid-state array lidars based on electrical analog delay

Xuanquan Wang, Ping Song*, Wuyang Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

As a typical application of indirect-time-of-flight (ToF) technology, photonic mixer device (PMD) solid-state array Lidar has gained rapid development in recent years. With the advantages of high resolution, frame rate and accuracy, the equipment is widely used in target recognition, simultaneous localization and mapping (SLAM), industrial inspection, etc. The PMD Lidar is vulnerable to several factors such as ambient light, temperature and the target feature. To eliminate the impact of such factors, a proper calibration is needed. However, the conventional calibration methods need to change several distances in large areas, which result in low efficiency and low accuracy. To address the problems, this paper presents an improved calibration method based on electrical analog delay. The method firstly eliminates the lens distortion using a self-adaptive interpolation algorithm, meanwhile it calibrates the grayscale image using an integral time simulating based method. Then, the grayscale image is used to estimate the parameters of ambient light compensation in depth calibration. Finally, by combining four types of compensation, the method effectively improves the performance of depth calibration. Through several experiments, the proposed method is more adaptive to multiscenes with targets of different reflectivities, which significantly improves the ranging accuracy and adaptability of PMD Lidar.

Original languageEnglish
Article number7329
Pages (from-to)1-22
Number of pages22
JournalSensors
Volume20
Issue number24
DOIs
Publication statusPublished - 2 Dec 2020

Keywords

  • Depth calibration method
  • Electrical analog delay
  • Joint calibration algorithm
  • PMD solid-state array Lidar
  • Photonic mixer device
  • Self-adaptive grayscale correlation

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