The IMU Augment SLAM on Unmanned Vehicle for Detection of Protective Measures in COVID-19

Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the tradition...

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Vydáno v:IEEE Sensors Journal Ročník 23; číslo 2; s. 933 - 946
Hlavní autoři: Liang, Siyuan, Zhu, Weiyue, Chakraborty, Chinmay, Du, Jianbo, Yu, Keping
Médium: Journal Article
Jazyk:angličtina
japonština
Vydáno: New York IEEE 15.01.2023
Institute of Electrical and Electronics Engineers (IEEE)
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Cameras
Coronaviruses
Cost function
COVID-19
Datasets
Feature extraction
Goggles
Gyroscopes
Inertial coordinates
LSD algorithm based on a conditional selection strategy
Noise reduction
Optimization
Pose estimation
Protective clothing
Real time
Real-time systems
Sensors
Simultaneous localization and mapping
SLAM
tight coupling
Unmanned vehicles
Visualization
wavelet threshold denoising based on the 3σ criterion
ISSN:1530-437X, 2379-9153, 1558-1748
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Popis
Shrnutí:Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the traditional manual detection. In this paper, a tightly-coupled nonlinear optimization approach is used to augment the visual feature extraction of SLAM by the gyroscope of the IMU to obtain a high-precision visual inertial system for joint position and pose estimation. Based on the VINS-Mono frame, first, an LSD algorithm based on a conditional selection strategy is proposed to extract line features efficiently. Then, we propose recovering missing point features from line features. Moreover, we propose a strategy to recover vanishing point features from line features, and add residuals to the SLAM cost function based on optimization, which optimizes point-line features in real time to promote the tracking and matching accuracy. Second, the wavelet threshold denoising method based on the <inline-formula> <tex-math notation="LaTeX">3\sigma </tex-math></inline-formula> criterion is used to carry out real-time online denoising for gyroscope to improve the output precision. Our WD-PL-VINS was measured on publicly available EuRoC datasets, TUM VI datasets and evaluated and validated in lab testing with a unmanned vehicle (UV) based on the NVIDIA Jetson-TX2 development board. The results show that our method's APE and RPE on MH_03_easy sequences are improved by 69.28% and 97.66%, respectively, compared with VINS-Mono.
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ISSN:1530-437X
2379-9153
1558-1748
DOI:10.1109/JSEN.2022.3189033