Extrinsic calibration of a single line scanning lidar and a camera

Kwak,; Huber,; Badino,; Kanade,

doi:10.1109/iros.2011.6048131

Cited by 37 publications

(10 citation statements)

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“…A classical tool box of LiDAR-to-Camera calibration is offered by [11], which is a very useful tool to compute the relationship between LiDAR and camera. Similar works are carried out in [8]. In this paper, a special checkerboard is designed to locate the LiDAR point position.…”

Section: Related Workmentioning

confidence: 89%

“…The appeared approaches can be generally categorized into two classes: The first class translates the LiDAR coordinate directly to the vehicle coordinate [6,14,15]. The second class is a LiDAR-to-Camera calibration method, in which the 3-D point data is projected into image coordinate first, then the fusing data is translated into vehicle coordinate according to extrinsic parameter of the camera [8,9].…”

Section: Related Workmentioning

confidence: 99%

“…Thus the extrinsic parameter calibration of LiDAR was always ignored. On the other hand, many researchers were interested in investigating LiDAR calibration to a camera for data fusion purposes [8][9][10][11]. In those approaches, they didn't map the LiDAR data into vehicle coordinate directly.…”

Section: Introductionmentioning

confidence: 99%

“…Then, these fused data are translated into vehicle coordinate according to the extrinsic parameter of the camera. In those approaches, some specific checkerboards are needed [8,12], or some specific equipment is required to make the LiDAR's trace visible [13,14].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair

Shang

Shi

et al. 2014

J Intell Robot Syst

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Light Detection And Ranging (LiDAR) has been widely employed in Unmanned Ground Vehicle (UGV) for autonomous navigation and object detection. In this paper, an efficient extrinsic parameter calibration approach, which is based on a pair of orthogonal normal vectors, is presented for an arbitrary equipped 3-D LiDAR. With the proposed approach, the whole calibration process can be easily and efficiently implemented in outdoor urban environment and no calibration equipment is required. The main advantages of this approach are twofold: (1) compared with traditional ways, the proposed approach employs an orthogonal normal vector pair, which is generated by ground plane and vertical wall in urban environment, so calibration equipments are not required anymore; (2) the normal vector is estimated from the point cloud data on a surface, thus a quite robust and accuracy estimation can be obtained. Experiments illustrate the effective and efficient performance of the proposed approach, compared with the state of the art.

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Section: Related Workmentioning

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair

Shang

Shi

et al. 2014

J Intell Robot Syst

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“…Once the object is observed simultaneously with a camera (The extrinsic calibration of a LIDAR and a camera is achieved by using the algorithms in Kwak et al 2011), we extract the object image I corresponding to the LIDAR geometry. The object image is divided into n sub-images I i that directly corresponding toL i of sub-surfaces of an object's surface.…”

Section: Appearance Representation Using Approximate Shapementioning

confidence: 99%

Online Approximate Model Representation Based on Scale-Normalized and Fronto-Parallel Appearance

et al. 2015

Self Cite

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Various object representations have been widely used for many tasks such as object detection, recognition, and tracking. Most of them requires an intensive training process on large database which is collected in advance, and it is hard to add models of a previously unobserved object which is not in the database. In this paper, we investigate how to create a representation of a new and unknown object online, and how to apply it to practical applications like object detection and tracking. To make it viable, we utilize a sensor fusion approach using a camera and a single-line scan LIDAR. The proposed representation consists of an approximated geometry model and a viewpoint-scale invariant appearance model which makes to extremely simple to match the model and the observation. This property makes it possible to model a new object online, and provides a robustness to viewpoint variation and occlusion. The representation has benefits of both an implicit model (referred to as a view-based model) Communicated by V. Lepetit. Electronic supplementary materialThe online version of this article (and an explicit model (referred to as a shape-based model). Intensive experiments using synthetic and real data demonstrate the viability of the proposed object representation in both modeling and detecting/tracking objects.

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Registration of three‐dimensional scanning LiDAR sensors: An evaluation of model‐based and model‐free methods

D’Adamo

Phillips

McĂree

2018

Journal of Field Robotics

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Registration, also know as extrinsic calibration, is the process of determining the position and orientation of a sensor relative to a known frame of reference. For ranging sensors such as light detection and ranging (LiDAR) used in field robotic applications, the quality of the registration determines the utility of the range measurements. This paper makes two contributions. The first is the introduction of a new method, termed maximum sum of evidence (MSoE) for registering three‐dimensional LiDAR sensors to moving platforms. This method is shown to produce more accurate registration solutions than two leading methods for these sensors, the adaptive structure registration filter (ASRF) and Rényi quadratic entropy (RQE). The second contribution of the paper is to study the accuracy of the MSoE registration against these two other approaches. One of these, like the MSoE, requires a truth model of the environment. The other, a model‐free method, seeks the registration that minimizes the RQE of a compound point cloud. The main finding of this investigation is that while the model‐based methods prove more accurate than the model‐free approach, the results of all three methods are fit for their intended field robotic applications. This leads us to conclude that registration based on RQE is preferable in many, if not all, field robotic applications for reasons of convenience, since a truth model of the environment is not required.

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Extrinsic calibration of a single line scanning lidar and a camera

Cited by 37 publications

References 0 publications

An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair

An Efficient Calibration Approach for Arbitrary Equipped 3-D LiDAR Based on an Orthogonal Normal Vector Pair

Online Approximate Model Representation Based on Scale-Normalized and Fronto-Parallel Appearance

Registration of three‐dimensional scanning LiDAR sensors: An evaluation of model‐based and model‐free methods

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