Improvements to Target-Based 3D LiDAR to Camera Calibration

Huang, Jiunn-Kai; Grizzle, Jessy W.

doi:10.1109/access.2020.3010734

Cited by 70 publications

(43 citation statements)

References 31 publications

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“…The studies of extrinsic calibration and the methodologies are well-established in the literature, see reference [141][142][143][144][145][146][147][148][149] for example. However, the extrinsic calibration of multiple sensors with different physical measurement principles can pose a challenge in multi-sensor systems.…”

Section: Extrinsic Calibration Overviewmentioning

confidence: 99%

“…An overview of the ROS topic message types as input requirements for each calibration board detector node, namely monocular camera detector (mono_detector), LiDAR detector (lidar_detector), stereo camera detector (stereo_detector), and radar detector (radar_detector). Based on reference [143] and [149]. A detailed overview of the ROS sensor message types are available in reference [160].…”

mentioning

confidence: 99%

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Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong¹,

Velasco-Hernandez²,

Barry³

et al. 2021

Preprint

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The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

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Section: Extrinsic Calibration Overviewmentioning

confidence: 99%

mentioning

confidence: 99%

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Yeong¹,

Velasco-Hernandez²,

Barry³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Due to the sparse vertical resolution of LiDARs, accurate corner feature extraction is difficult if the chessboard is far from the LiDAR or when a low channel LiDAR is used. Many researchers have published papers using various calibration boards [19][20][21][22][23][24][25][26][27] to solve this problem. Park et al [14] proposed a method for camera-LiDAR calibration by detecting the edge of a polygonal plane and obtaining the cross points at which the edges intersect.…”

Section: A Artificial Marker-based Approachesmentioning

confidence: 99%

“…Based on the method of [14], several papers were published to extract more accurate cross points from polygonal planes using random sample consensus (RANSAC) [19][20]. Huang et al [21][22] suggested an automatic camera-LiDAR calibration based on a polygonal marker, but this method does not work well with 16 channel LiDAR.…”

Section: A Artificial Marker-based Approachesmentioning

confidence: 99%

Automatic Multiple LiDAR Calibration Based on the Plane Features of Structured Environments

Kim

2021

IEEE Access

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The multiple light detection and ranging (LiDAR) system has been attached to numerous autonomous vehicles to reduce blind spot risks and increase measurement resolution. It is also important to accurately aligning multi-LiDAR data because misaligned sensor data can affect the localization and perception algorithms of LiDAR sensors. However, precise extrinsic parameter estimation is a challenging research area because the feature points in LiDAR data cannot be precisely aligned owing to the LiDAR resolution. In this paper, we proposed a targetless multi-LiDAR calibration method without any initial information. In the proposed approach, a random sample consensus (RANSAC)-based plane fitting technique was utilized for plane extraction, and cross-feature points were extracted from the polar view transform of segmented planes. To resolve the discontinuity problem of optimization, a nonlinear optimization method was utilized, and the experiments were performed within a structured environment. Overall, the experimental results showed that the proposed method yields comparable results with state-of-theart methods.

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“…The same wooden rectangular target of size A1 sheet of paper (0.841m x 0.594m = 0.500m 2 ) is scanned indoors at distances ranging from 5 to 40 meters with a 5-meter interval as shown in the first row in Figure 4. As indicated in (Huang and Grizzle, 2020), placing the target so that its top and bottom edges run parallel to the lidar's rings lead to ambiguity in the vertical position of the target caused by the increasing spacing of the rings along with the distance. Therefore, the target is slightly rotated.…”

Section: Scanning a Targetmentioning

confidence: 99%

Comparison of accuracy and density of static and mobile laser scanners

Trzeciak¹,

Brilakis²

2021

Proceedings of the 2021 European Conference on Computing in Construction

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We test the accuracy and density of mobile lidar-based scanners and compare them with the performance of a static scanner. This is achieved by systematically scanning a rectangular target at a growing distance, collating the statistics of the scans and comparing them. This study shows that the accuracy of the scans outputted by the static scanner is about 20 times better at 5 meters than those produced by the mobile devices and this gap further increases along with the distance. The density, measured in points per second, drops along with the distance for all the tested devices, with mobile scanners outperforming their static counterparts.

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Improvements to Target-Based 3D LiDAR to Camera Calibration

Abstract: reflects the opinions and conclusions of its authors and not the funding entities. Dr. M Ghaffari offered advice during the course of this project. The first author thanks Wonhui Kim for useful conversations and Ray Zhang for generating the semantic image.

Cited by 70 publications

References 31 publications

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Automatic Multiple LiDAR Calibration Based on the Plane Features of Structured Environments

Comparison of accuracy and density of static and mobile laser scanners

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