Infrastructure Based Calibration of a Multi-Camera and Multi-LiDAR System Using Apriltags

Xie, Yuanfan; Shao, Rui; Guli, Popo; Li, Bo; Wang, Liang

doi:10.1109/ivs.2018.8500646

Cited by 39 publications

(18 citation statements)

References 17 publications

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“…Artificial landmarks are prevalently used to find correspondences among sensor data. Xie et al [16] provided a general solution to jointly calibrate multiple cameras and lidars in the presence of a pre-built environment with apriltags. Steder et al [17] proposed a tracking-based method to calibrate multiple 2D lidars using a moving object which appears in their overlapping areas.…”

Section: Related Work a Calibration For Multi-lidar Systemsmentioning

confidence: 99%

A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

Jiao

Liao

Zhu

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems. However, precise calibration of multiple lidars is challenging since the feature correspondences in scan points cannot always provide enough constraints. To address this problem, the existing methods require fixed calibration targets in scenes or rely exclusively on additional sensors. In this paper, we present a novel method that enables automatic lidar calibration without these restrictions. Three linearly independent planar surfaces appearing in surroundings is utilized to find correspondences. Two components are developed to ensure the extrinsic parameters to be found: a closed-form solver for initialization and an optimizer for refinement by minimizing a nonlinear cost function. Simulation and experimental results demonstrate the high accuracy of our calibration approach with the rotation and translation errors smaller than 0.05rad and 0.1m respectively.

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Section: Related Work a Calibration For Multi-lidar Systemsmentioning

confidence: 99%

A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

Jiao

Liao

Zhu

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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“…However, reliable and accurate camera calibration usually requires an expert intuition to reliably constrain all of the parameters in the camera model. Existing calibration toolboxes [8,9] ask users to capture images from a posed calibration pattern board (chessboard [5,10,11], circle grid pattern [12], AprilTag [13,14], etc.) in positions of their choosing, after which the maximum-likelihood calibrations parameters are computed using all images in a batch optimization.…”

Section: Introductionmentioning

confidence: 99%

Camera Calibration with Pose Guidance

Ren

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Camera calibration plays a critical role in various computer vision tasks such as autonomous driving or augmented reality. Widely used camera calibration tools utilize plane pattern based methodology, such as using a chessboard or AprilTag board, user's calibration expertise level significantly affects calibration accuracy and consistency when without clear instruction. Furthermore, calibration is a recurring task that has to be performed each time the camera is changed or moved. It's also a great burden to calibrate huge amounts of cameras such as Driver Monitoring System (DMS) cameras in a production line with millions of vehicles. To resolve above issues, we propose a calibration system called Calibration with Pose Guidance to improve calibration accuracy, reduce calibration variance among different users or different trials of the same person. Experiment result shows that our proposed method achieves more accurate and consistent calibration than traditional calibration tools.

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“…In order to calibrate multiple sensors at the same time, large scale infrastructures are proposed by researchers [5], [6], [7]. The key motivation is to match data from multiple sensors to a shared infrastructure, which should be large enough to cover the view of all sensors.…”

Section: Introductionmentioning

confidence: 99%

Single-Shot is Enough: Panoramic Infrastructure Based Calibration of Multiple Cameras and 3D LiDARs

Fang¹,

Ding²,

Dong³

et al. 2021

Preprint

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The integration of multiple cameras and 3D Li-DARs has become basic configuration of augmented reality devices, robotics, and autonomous vehicles. The calibration of multi-modal sensors is crucial for a system to properly function, but it remains tedious and impractical for mass production. Moreover, most devices require re-calibration after usage for certain period of time. In this paper, we propose a single-shot solution for calibrating extrinsic transformations among multiple cameras and 3D LiDARs. We establish a panoramic infrastructure, in which a camera or LiDAR can be robustly localized using data from single frame. Experiments are conducted on three devices with different camera-LiDAR configurations, showing that our approach achieved comparable calibration accuracy with the state-of-the-art approaches but with much greater efficiency.

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Infrastructure Based Calibration of a Multi-Camera and Multi-LiDAR System Using Apriltags

Cited by 39 publications

References 17 publications

A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

A Novel Dual-Lidar Calibration Algorithm Using Planar Surfaces

Camera Calibration with Pose Guidance

Single-Shot is Enough: Panoramic Infrastructure Based Calibration of Multiple Cameras and 3D LiDARs

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