IP Library › Granted Patent US 12,248,103
Granted Patent B2
US 12,248,103 · App. 17/213,681 · Granted Mar 11, 2025

System and method for lidar defogging

Inventors: Tzu-Hsien Sang (Hsinchu, TW); Sung-You Tsai (New Taipei, TW); Tsung-Po Yu (Taichung, TW)
Assignee: NATIONAL YANG MING CHIAO TUNG UNIVERSITY
G01S7/497G01S7/4808G01S7/4861G06N20/00G01S2007/4977
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,248,103
App. No.
17/213,681
Granted
Mar 11, 2025
Kind
B2
Abstract

A system and method for LiDAR defogging is disclosed. The method comprises: applying a detection device to determine the fog status and generate a histogram; determining the fog concentration between a target location and the detection device in the histogram according to the histogram; and applying a defogging method to defog the fog concentration between the target location and the detecting device.

Claims (19)

1. A LiDAR defogging system, comprising:

a transmitter configured to emit a detection signal toward a field of view of the LiDAR defogging system;

a detector configured to receive an echo signal reflected from an object in the field of view of the LiDAR defogging system; and

an instrument configured to:

generate a first histogram based on the detection signal and the echo signal, wherein the x-axis of the first histogram represents as at least one time slot between the time for emitting the detection signal and the time for receiving the echo signal, and the y-axis of the first histogram represents as energy information of the echo signal;

determine a concentration of fog between the detector and the object by comparing the first histogram and a second histogram generated by collecting blank channel information, wherein the blank channel information is derived by signal reflected from the fog; and

remove an interference of the fog from the echo signal according to the concentration of the fog.

2. The LiDAR defogging system according to claim 1 , wherein the transmitter is further configured to emit the detection signal in a form of laser pulsed light wave.

3. The LiDAR defogging system according to claim 1 , wherein the instrument is further configured to:

estimate a distance between the object and the LiDAR defogging system.

4. A defogging method for a LiDAR defogging system, comprising:

emitting a detection signal toward a field of view of the LiDAR defogging system;

receiving an echo signal reflected from an object in the field of view of the LiDAR defogging system;

generating a first histogram based on the detection signal and the echo signal, wherein the x-axis of the first histogram represents as at least one time slot between the time for emitting the detection signal and the time for receiving the echo signal, and the y-axis of the first histogram represents as energy information of the echo signal;

determining a concentration of fog between the LiDAR defogging system and the object by comparing the first histogram and a second histogram generated by collecting blank channel information of the detector, wherein the blank channel information is derived by signal reflected from the fog; and

removing an interference of the fog from the echo signal according to the concentration of the fog.

5. The defogging method according to claim 4 , wherein the detection signal is emitted in a form of laser pulsed light wave.

6. The LiDAR defogging method according to claim 4 , further comprising:

estimating a distance between the object and the LiDAR defogging system.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 29, 2021
From: SANG, TZU-HSIEN; TSAI, SUNG-YOU; YU, TSUNG-PO
To: NATIONAL YANG MING CHIAO TUNG UNIVERSITY
Reel/Frame 055753/0831 →
Priority Claims (1)
TW 109110394 · Mar 27, 2020 · national
Continuity (1)
Related Publication 20210302554A1 · Sep 30, 2021
References Cited (14)
US 20190361100A1 · Abari · 2019 [cited by examiner]
US 20210025990A1 · Ozawa et al. · 2021 [cited by applicant]
TW 1673190B · 2019 [cited by applicant]
TW 201939009A · 2019 [cited by applicant]
TW 202001288A · 2020 [cited by applicant]
WO WO2019167485A1 · 2019 [cited by applicant]
Shamsudin (doc. “Fog removal using laser beam penetration, laser intensity, and geometrical features for 3D measurements in fog-filled room” (Year: 2016). [cited by examiner]
Li et al. (doc“What happens for a ToF LiDAR in fog?” (Year: 2020). [cited by examiner]
Bansal et al. (doc. “A Review of Image Restoration based Image Defogging Algorithms”). (Year: 2017). [cited by examiner]
Asvadi et al., “DepthCN: Vehicle detection using 3D-LIDAR and ConvNet”, IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, total 6 pages. [cited by applicant]
Satat et al., “Towards photography through realistic fog”, 2018 IEEE International Conference on Computational Photography, total 10 pages. [cited by applicant]
Shamsudin et al., “Fog removal using laser beam penetration, laser intensity, and geometrical features for 3D measurements in fog-filled room”, Advanced Robotics, Apr. 2016, pp. 729-743. [cited by applicant]
Song et al., “The irradiating field of view of imaging laser radar under fog conditions in a controlled laboratory environment”, Journal of Optics, 2017, vol. 19, No. 4, pp. 1-8. [cited by applicant]
Zorzi et al., “Full-waveform airborne LiDAR data classification using convolutional neural networks”, IEEE Transactions on Geoscience and Remote Sensing, 2019, total 7 pages. [cited by applicant]