A Survey of Extrinsic Parameters Calibration Techniques for Autonomous Devices

Nie, Jiwei; Pan, Feng; Xue, Dingyü; Luo, Ling

doi:10.1109/ccdc52312.2021.9602601

Cited by 6 publications

(9 citation statements)

References 37 publications

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“…There exist multiple surveys for LiDAR-camera calibration. Nie et al (2021) categorized calibration methods into offline and online methods. Yaopeng et al (2021) divided them into manual calibration and automatic calibration solutions.…”

Section: Categories Of Extrinsic Calibration Methodsmentioning

confidence: 99%

“…Though there are already a few surveys on LiDAR-camera calibration such as (Khurana and Nagla, 2021, Nie et al, 2021, Wang et al, 2021, Yaopeng et al, 2021, they usually cover a wide range of traditional calibration methods and only provide a relatively brief review of automatic targetless calibration. Given its rapidly increasing popularity, we believe a thorough and insightful review on automatic targetless LiDAR-camera calibration is both imperative and important.…”

Section: Introductionmentioning

confidence: 99%

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Automatic targetless LiDAR–camera calibration: a survey

Xiao

Wang³

et al. 2022

Artif Intell Rev

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The recent trend of fusing complementary data from LiDARs and cameras for more accurate perception has made the extrinsic calibration between the two sensors critically important. Indeed, to align the sensors spatially for proper data fusion, the calibration process usually involves estimating the extrinsic parameters between them. Traditional LiDAR-camera calibration methods often depend on explicit targets or human intervention, which can be prohibitively expensive and cumbersome. Recognizing these weaknesses, recent methods usually adopt the autonomic targetless calibration approach, which can be conducted at a much lower cost. This paper presents a thorough review of these automatic targetless LiDAR-camera calibration methods. Specifically, based on how the potential cues in the environment are retrieved and utilized in the calibration process, we divide the methods into four categories: information theory based, feature based, ego-motion based, and learning based methods. For each category, we provide an in-depth overview with insights we have gathered, hoping to serve as a potential guidance for researchers in the related fields.

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Section: Categories Of Extrinsic Calibration Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic targetless LiDAR–camera calibration: a survey

Xiao

Wang³

et al. 2022

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…This paper is focused on the calibration in space, which is to obtain the rigid transformation between the two sensors. We also assume that temporal calibration has been performed well just like other papers [ 1 , 4 , 16 ].…”

Section: Problem Formulationmentioning

confidence: 99%

“…According to the need for calibration targets, extrinsic calibration between a LiDAR sensor and a camera can be divided into the following two categories [ 1 , 2 , 4 ]:…”

Section: Problem Formulationmentioning

confidence: 99%

“…With the gradual maturity and commercialization of LiDAR technology, LiDAR and camera have become the most common and fundamental sensor combination in unmanned driving and autonomous robot navigation, and are currently the most typical multi-sensor fusion devices. This technology has been widely used in various challenging tasks, such as object detection and tracking [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ], simultaneous localization and mapping [ 9 , 10 , 11 ], and autonomous navigation [ 12 , 13 ]. In order to achieve effective and accurate data fusion, it is necessary to calibrate the extrinsic parameters of LiDAR and the camera, namely to estimate the transformation between the two sensors in a common reference frame.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Deep Learning-Based LiDAR and Camera Extrinsic Calibration

Tan,

Zhang,

Teng

et al. 2024

Sensors

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Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera data fusion of the autonomous system. This technology is widely used in fields such as autonomous driving, mobile robots, intelligent surveillance, and visual measurement. The learning-based method is one of the targetless calibrating methods in LiDAR and camera calibration. Due to its advantages of fast speed, high accuracy, and robustness under complex conditions, it has gradually been applied in practice from a simple theoretical model in just a few years, becoming an indispensable and important method. This paper systematically summarizes the research and development of this type of method in recent years. According to the principle of calibration parameter estimation, learning-based calibration algorithms are divided into two categories: accurate calibrating estimation and relative calibrating prediction. The evolution routes and algorithm frameworks of these two types of algorithms are elaborated, and the methods used in the algorithms’ steps are summarized. The algorithm mechanism, advantages, limitations, and applicable scenarios are discussed. Finally, we make a summary, pointing out existing research issues and trends for future development.

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