Wide range linearization calibration method for DFB Laser in FMCW LiDAR

Zhang, Jiatong; Liu, Chang; Su, Liwen; Fu, Xinghu; Jin, Wa; Bi, Weihong; Fu, Guangwei

doi:10.1016/j.optlaseng.2023.107961

Cited by 1 publication

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combined with the modulation rate, the detection distance is finally obtained, which has the advantages of low power consumption, long range, and high accuracy [ 9 ]. However, the current development of frequency modulation technology is immature, since the commonly used direct-modulated semiconductor lasers require iterative learning to improve the linearity of frequency modulation and are easily affected by environmental factors [ 10 , 11 ]. If single-sideband modulation is used, linearity is improved, yet the chip loss remains large such that the output power is low, leading to a limited detection range [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network-Based Phase-Modulated Continuous-Wave LiDAR

Zhang,

Wang,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

LiDAR has high accuracy and resolution and is widely used in various fields. In particular, phase-modulated continuous-wave (PhMCW) LiDAR has merits such as low power, high precision, and no need for laser frequency modulation. However, with decreasing signal-to-noise ratio (SNR), the noise on the signal waveform becomes so severe that the current methods to extract the time-of-flight are no longer feasible. In this paper, a novel method that uses deep neural networks to measure the pulse width is proposed. The effects of distance resolution and SNR on the performance are explored. Recognition accuracy reaches 81.4% at a 0.1 m distance resolution and the SNR is as low as 2. We simulate a scene that contains a vehicle, a tree, a house, and a background located up to 6 m away. The reconstructed point cloud has good fidelity, the object contours are clear, and the features are restored. More precisely, the three distances are 4.73 cm, 6.00 cm, and 7.19 cm, respectively, showing that the performance of the proposed method is excellent. To the best of our knowledge, this is the first work that employs a neural network to directly process LiDAR signals and to extract their time-of-flight.

show abstract