Extrinsic Calibration of Visual and Inertial Sensors for the Autonomous Vehicle

Wang, Zhoubo; Zhang, Zuhong; Hu, Xuehai; Zhu, Wei; Deng, Hongbin

doi:10.1109/jsen.2023.3282211

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Reference 29 focuses on the extrinsic calibration of visual and inertial sensors for autonomous vehicles. The authors present a method for accurately calibrating the relative positions and orientations of both types of sensors to facilitate robust perception and navigation.…”

Section: The Proposed Algorithm Formulationmentioning

confidence: 99%

A fast strapdown gyrocompassing algorithm based on INS differential errors

Atashgah,

Mohammadkarimi,

Ebrahimi

2023

Sci Rep

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This paper presents an enhanced algorithm for inertial gyrocompassing using strapdown sensors, which performs faster than the other available ones. The proposed algorithm is based on differential errors in an inertial navigation system and requires only the output of the inertial measurement unit while extracting and compensating for the inertial sensor errors. After eliminating the error of the inertial sensors, which is accomplished swiftly, the coarse alignment algorithm performs with error-compensated sensors, and the true north is extracted accurately. The number of non-observable parameters of the algorithm is equal to that of the fine alignment algorithm; therefore, its accuracy is the same as that of a well-tuned fine alignment. Numerical simulations and lab experiments demonstrate that the proposed method performs heading estimation in the time required to perform the coarse alignment, which is faster than the existing fine alignment algorithms.

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Section: The Proposed Algorithm Formulationmentioning

confidence: 99%

A fast strapdown gyrocompassing algorithm based on INS differential errors

Atashgah,

Mohammadkarimi,

Ebrahimi

2023

Sci Rep

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A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions

Matos,

Bernardino,

Durães

et al. 2024

Sensors

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Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle’s behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor’s failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge.

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Extrinsic Calibration of Visual and Inertial Sensors for the Autonomous Vehicle

Cited by 2 publications

References 22 publications

A fast strapdown gyrocompassing algorithm based on INS differential errors

A fast strapdown gyrocompassing algorithm based on INS differential errors

A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions

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