Online Intelligent Calibration of Cameras and LiDARs for Autonomous Driving Systems

Xu, Hanbo; Lan, Gongjin; Wu, Shaoguan; Hao, Qi

doi:10.1109/itsc.2019.8916872

Cited by 31 publications

(20 citation statements)

References 20 publications

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“…The BEA runs BO in the first stage, therefore, the first stage of the BEA may theoretically inherit the defect of BO that its efficiency drops once the number of variables is increased in a large number. Fourth, we aim to apply BEA to efficiently learn controllers in real time on real robots with various evolved morphologies and other tasks such as evolutionary predator-prey robot systems [47,48] , autonomous driving [49] .…”

Section: Discussionmentioning

confidence: 99%

Time efficiency in optimization with a bayesian-Evolutionary algorithm

Lan

Tomczak

Roijers

et al. 2022

Swarm and Evolutionary Computation

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Section: Discussionmentioning

confidence: 99%

Time efficiency in optimization with a bayesian-Evolutionary algorithm

Lan

Tomczak

Roijers

et al. 2022

Swarm and Evolutionary Computation

Self Cite

View full text Add to dashboard Cite

“…In the future, we will combine the quality and computation time into a measurement that can be used to evaluate the optimization algorithms comprehensively. In addition, we will consider the results of quality and computation time for the optimization in artificial intelligent applications of robotics [10], [11], intelligent sensing [23], [24], virtual reality [25], [26], autonomous driving [27] to improve the performance of these studies.…”

Section: Discussionmentioning

confidence: 99%

Quality and Computation Time in Optimization Problems

He¹

2021

Preprint

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Optimization problems are crucial in artificial intelligence. Optimization algorithms are generally used to adjust the performance of artificial intelligence models to minimize the error of mapping inputs to outputs. Current evaluation methods on optimization algorithms generally consider the performance in terms of quality. However, not all optimization algorithms for all test cases are evaluated equal from quality, the computation time should be also considered for optimization tasks. In this paper, we investigate the quality and computation time of optimization algorithms in optimization problems, instead of the one-for-all evaluation of quality. We select the well-known optimization algorithms (Bayesian optimization and evolutionary algorithms) and evaluate them on the benchmark test functions in terms of quality and computation time. The results show that BO is suitable to be applied in the optimization tasks that are needed to obtain desired quality in the limited function evaluations, and the EAs are suitable to search the optimal of the tasks that are allowed to find the optimal solution with enough function evaluations. This paper provides the recommendation to select suitable optimization algorithms for optimization problems with different numbers of function evaluations, which contributes to the efficiency that obtains the desired quality with less computation time for optimization problems.

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“…In solving the homogeneous transformation equation for the rotation parameter, the use of an angle-axis representation can simplify the process (Taylor and Nieto, 2016). • Lie algebra: The rotation parameters can also be expressed in the form of Lie algebras (Xu et al, 2019), which is suitable for optimization problems. Lie algebra specifies the extrinsic parameter through a vector with 6 degrees of freedom (DoF) variables.…”

Section: Hand-eye Based Methodsmentioning

confidence: 99%

“…In specific, these edges are recognized from the points by calculating the differences in depth between neighboring points and filtering out points whose values of differences are below a pre-set threshold (Levinson and Thrun, 2013). This idea has been widely applied in various edge extraction methods (Banerjee et al, 2018, Blaga and Nedevschi, 2017, Ma et al, 2021a, Munoz-Banon et al, 2020, Xu et al, 2019. The idea can be further extended by first generating a dense depth map by upsampling the point cloud, then identifying the edges by calculating gradient changes in depth (Castorena et al, 2016).…”

Section: Feature Extractionmentioning

confidence: 99%

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.

show abstract

Online Intelligent Calibration of Cameras and LiDARs for Autonomous Driving Systems

Cited by 31 publications

References 20 publications

Time efficiency in optimization with a bayesian-Evolutionary algorithm

Time efficiency in optimization with a bayesian-Evolutionary algorithm

Quality and Computation Time in Optimization Problems

Automatic targetless LiDAR–camera calibration: a survey

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