Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Lee, Woosik; Cho, Hyojoo; Hyeong, Seungho; Chung, Woojin

doi:10.3390/s19194236

Cited by 12 publications

(10 citation statements)

References 17 publications

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“…The field of human-robot interaction involves designing, developing, and evaluating strategies to help and improve human-robot capabilities and skills together [ 44 ]. HRI enabled robots are currently used in urban search and rescue [ 45 ].…”

Section: Resultsmentioning

confidence: 99%

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Pishgar

Issa

Sietsema

et al. 2021

IJERPH

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Introduction: The field of artificial intelligence (AI) is rapidly expanding, with many applications seen routinely in health care, industry, and education, and increasingly in workplaces. Although there is growing evidence of applications of AI in workplaces across all industries to simplify and/or automate tasks there is a limited understanding of the role that AI contributes in addressing occupational safety and health (OSH) concerns. Methods: This paper introduces a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) that highlights the role that AI plays in the anticipation and control of exposure risks in a worker’s immediate environment. Two hundred and sixty AI papers across five sectors (oil and gas, mining, transportation, construction, and agriculture) were reviewed using the REDECA framework to highlight current applications and gaps in OSH and AI fields. Results: The REDECA framework highlighted the unique attributes and research focus of each of the five industrial sectors. The majority of evidence of AI in OSH research within the oil/gas and transportation sectors focused on the development of sensors to detect hazardous situations. In construction the focus was on the use of sensors to detect incidents. The research in the agriculture sector focused on sensors and actuators that removed workers from hazardous conditions. Application of the REDECA framework highlighted AI/OSH strengths and opportunities in various industries and potential areas for collaboration. Conclusions: As AI applications across industries continue to increase, further exploration of the benefits and challenges of AI applications in OSH is needed to optimally protect worker health, safety and well-being.

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Section: Resultsmentioning

confidence: 99%

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Pishgar

Issa

Sietsema

et al. 2021

IJERPH

View full text Add to dashboard Cite

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“…The online calibration method is more convenient and can effectively deal with the dynamic changes of calibration parametersand provide more accurate calibration parameters for the system. Thus, t r can be self-calibrated online in our system instead of offline [24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Point feature correction based rolling shutter modeling for EKF-based visual-inertial odometry

Zhang,

Zhang

2023

Meas. Sci. Technol.

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The application of consumer-grade rolling-shutter (RS) camera in VIO system deserves attention. In previous works, VIO systems with RS camera usually adopt different dimensions of RS modeling to represent the camera motion. Although the complex camera motion expression improves the accuracy, adding more variables greatly increases the computation load. Therefore, considering the limitations of camera motion modeling, this paper introduces a point feature correction strategy to improve performance of the system in both speed and accuracy. The optimized camera poses and high-frequency IMU are employed to correct the point features on RS image to new pixel coordinates in global-shutter view (at the middle of RS image readout time) by epipolar transfer. At the same time, we handle the zero velocity and abnormal cases of RS modeling to improve the robustness of the system. Furthermore, the readout time of the RS camera is online self-calibrated in the stage of state augmentation and can quickly converge and stabilize to near the true value. And the readout time of the RS camera is locally observable, except in two degenerate motions.  Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on a public dataset in terms of accuracy and computational cost. We port our algorithm to android-based mobile phone and our system outputs real-time trajectories on mobile phone. Then, we use the motion capture system to obtain the ground truth phone pose to test the performance of our algorithm.

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“…The general approach to building topological maps is to precisely estimate the vehicle trajectories and then accumulate the sensory data accordingly [3]. The trajectory estimation can be achieved using GNSS/INS-RTK (GIR) systems, SLAM technologies, or Dead Reckoning (DR) based on the vehicle velocity and time interval between positions [4][5][6][7][8]. The topological illustration can be achieved by encoding dense sensory representations, such as 3D point clouds and 2D grid maps, or sparse descriptions, such as feature-based maps [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images

Aldibaja,

Yanase,

Suganuma

2024

Sensors

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Lane graphs are very important for describing road semantics and enabling safe autonomous maneuvers using the localization and path-planning modules. These graphs are considered long-life details because of the rare changes occurring in road structures. On the other hand, the global position of the corresponding topological maps might be changed due to the necessity of updating or extending the maps using different positioning systems such as GNSS/INS-RTK (GIR), Dead-Reckoning (DR), or SLAM technologies. Therefore, the lane graphs should be transferred between maps accurately to describe the same semantics of lanes and landmarks. This paper proposes a unique transfer framework in the image domain based on the LiDAR intensity road surfaces, considering the challenging requirements of its implementation in critical road structures. The road surfaces in a target map are decomposed into directional sub-images with X, Y, and Yaw IDs in the global coordinate system. The XY IDs are used to detect the common areas with a reference map, whereas the Yaw IDs are utilized to reconstruct the vehicle trajectory in the reference map and determine the associated lane graphs. The directional sub-images are then matched to the reference sub-images, and the graphs are safely transferred accordingly. The experimental results have verified the robustness and reliability of the proposed framework to transfer lane graphs safely and accurately between maps, regardless of the complexity of road structures, driving scenarios, map generation methods, and map global accuracies.

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Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Cited by 12 publications

References 17 publications

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

REDECA: A Novel Framework to Review Artificial Intelligence and Its Applications in Occupational Safety and Health

Point feature correction based rolling shutter modeling for EKF-based visual-inertial odometry

Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images

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