Extrinsic Calibration Methods for Laser Range Finder and Camera: A Systematic Review

Khurana, Archana; Nagla, K. S.

doi:10.1007/s12647-021-00500-x

Cited by 15 publications

(10 citation statements)

References 191 publications

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“…Yaopeng et al (2021) divided them into manual calibration and automatic calibration solutions. Later, Khurana and Nagla (2021) classified existing calibration methods based on: (1) 2D or 3D LiDAR, (2) target-based or targetless, and (3) manual or automatic. Wang et al (2021) Considered the need for targets and geometric constraints as transverse and longitudinal dimensions to classify the calibration methods.…”

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

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“…The literature can be classified into two categories based on the types of range finders employed with the intention of extrinsic calibration between the camera and the rangefinder: beams that can be seen and unseen (Kim and Ha 2020). Regarding the calibration methods, (Khurana and Nagla 2021) offered an exhaustive review of all current calibration techniques for calibrating LRF and camera extrinsically. Both sensors simultaneously examined either natural sceneries (targetless) or predetermined targets (target-based) in the approaches.…”

Section: Related Workmentioning

confidence: 99%

Extrinsic Calibration of Rotating 2d Laser Range Finder and Camera Using Photogrammetric Test Field and Ping Pong Balls

Manouchehri

Ahmadabadian²

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this article, a method for the extrinsic calibration of a 2D laser range finder and a camera is presented. This technique produces a 3D point cloud from the test field by connecting a laser range finder to a servomotor. In this study, ping balls and standard photogrammetric targets were employed as a test field. Ping pong balls are used because they can be easily recognized in data from laser range finder and camera. To calculate extrinsic calibration parameters between a camera and laser range finder, these balls are employed as control points in the data. The extrinsic calibration of the Laser range finder and camera is carried out using the point cloud created from the test field and the photos captured from the test field. In this method, a sphere is fitted to each ping pong ball's points in the 3D point cloud, and the coordinates of that sphere's center are taken to be the coordinates of that ball. By measuring the distances between various targets in the test field, the scale can be resolved. This approach was compared with another state-of-the-art method. The proposed method is more accurate and stable than the alternative way, taking into account the average inaccuracy of check points.

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“…However, due to assembly errors and internal sensor errors, the overall point cloud pro-vided by multiple sensors is often unaligned [4]. Therefore, before the robot is put into use, it is necessary to calibrate the external parameters of the depth sensor [5].…”

Section: Introductionmentioning

confidence: 99%

A novel joint depth sensor calibration method without fixture for mobile robots’ navigation

Lu,

Yuan,

Xue

2024

The Journal of Engineering

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Commercial mobile robots are usually equipped with multiple depth sensors that can measure the point cloud information around the robot's environment. The installation process of these sensors contains assembly error and sensor measurement error, so it is necessary to calibrate each sensor to align the point cloud. In order to obtain the sensor calibration results of commercial robots under normal working conditions, this study proposes a fixture free multi depth sensor joint calibration method that can be deployed on low‐cost embedded computing units, which efficiently aligns the point clouds of each sensor. During the calibration process, the robot is placed in the center of three upright thin plates perpendicular to the ground. 2D LIDAR depicts high‐precision contours of the upright thin plates. In the calibration process of each depth sensor, the roll angle and pitch angle of the sensor point cloud are first calibrated to make it perpendicular to the ground, and then the yaw angle and position of the point cloud are calibrated to fit the high‐precision contour of the upright thin plate. The results show that this method can be deployed on low‐cost embedded computing units, with real‐time and accurate calibration results. The convergence of calibration results can be achieved through up to 5 iterations, and the average running time is less than 120 ms. This research achievement provides a reference for multi‐sensor calibration of commercial robots.

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Extrinsic Calibration Methods for Laser Range Finder and Camera: A Systematic Review

Cited by 15 publications

References 191 publications

Automatic targetless LiDAR–camera calibration: a survey

Automatic targetless LiDAR–camera calibration: a survey

Extrinsic Calibration of Rotating 2d Laser Range Finder and Camera Using Photogrammetric Test Field and Ping Pong Balls

A novel joint depth sensor calibration method without fixture for mobile robots’ navigation

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