Indirect Correspondence-Based Robust Extrinsic Calibration of LiDAR and Camera

Sim, Sungdae; Sock, Juil; Kwak, Kiho

doi:10.3390/s16060933

Cited by 36 publications

(21 citation statements)

References 20 publications

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“…Despite recent developments, fusing multimodal sensory information is still a challenging problem [7]. Previously proposed solutions can provide a reasonable estimation of these parameters but they are limited due to their offline processing requirement [8] and a dedicated artificial calibration target present in a controlled environment [9].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park

Moghadam

Kim

et al. 2020

IEEE Robot. Autom. Lett.

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The demand for multimodal sensing systems for robotics is growing due to the increase in robustness, reliability and accuracy offered by these systems. These systems also need to be spatially and temporally co-registered to be effective. In this paper, we propose a targetless and structureless spatiotemporal camera-LiDAR calibration method. Our method combines a closed-form solution with a modified structureless bundle adjustment where the coarse-to-fine approach does not require an initial guess on the spatiotemporal parameters. Also, as 3D features (structure) are calculated from triangulation only, there is no need to have a calibration target or to match 2D features with the 3D point cloud which provides flexibility in the calibration process and sensor configuration. We demonstrate the accuracy and robustness of the proposed method through both simulation and real data experiments using multiple sensor payload configurations mounted to hand-held, aerial and legged robot systems. Also, qualitative results are given in the form of a colorized point cloud visualization.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Park

Moghadam

Kim

et al. 2020

IEEE Robot. Autom. Lett.

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“…To ensure optimal performance, the LiDAR and IMU sensors must be properly time delay calibrated, including estimates of the relative timing of each sensor measurement, coordinate transformation between the different sensors, and time delay calibration parameters estimation are required [1,2,3,4]. …”

Section: Introductionmentioning

confidence: 99%

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

Liu

2017

Sensors

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The time delay calibration between Light Detection and Ranging (LiDAR) and Inertial Measurement Units (IMUs) is an essential prerequisite for its applications. However, the correspondences between LiDAR and IMU measurements are usually unknown, and thus cannot be computed directly for the time delay calibration. In order to solve the problem of LiDAR-IMU time delay calibration, this paper presents a fusion method based on iterative closest point (ICP) and iterated sigma point Kalman filter (ISPKF), which combines the advantages of ICP and ISPKF. The ICP algorithm can precisely determine the unknown transformation between LiDAR-IMU; and the ISPKF algorithm can optimally estimate the time delay calibration parameters. First of all, the coordinate transformation from the LiDAR frame to the IMU frame is realized. Second, the measurement model and time delay error model of LiDAR and IMU are established. Third, the methodology of the ICP and ISPKF procedure is presented for LiDAR-IMU time delay calibration. Experimental results are presented that validate the proposed method and demonstrate the time delay error can be accurately calibrated.

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“…The calibration task is formulated as the registration of the corresponding observations from the camera and the laserscanner. Many of the existing approaches make use of special calibration objects like triangles, folding patterns, cubes or more complex calibration objects consisting of multiple connected planes Sim et al, 2016;Dong and Isler, 2016;Li et al, 2016;Chen et al, 2016;Kong et al, 2013;Yu et al, 2013). Most of these calibration objects are additionally equipped with checkerboards or markers.…”

Section: Related Workmentioning

confidence: 99%

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

Hillemann

Jutzi²

2017

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

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ABSTRACT:Unmanned Aerial Vehicle (UAV) with adequate sensors enable new applications in the scope between expensive, large-scale, aircraftcarried remote sensing and time-consuming, small-scale, terrestrial surveyings. To perform these applications, cameras and laserscanners are a good sensor combination, due to their complementary properties. To exploit this sensor combination the intrinsics and relative poses of the individual cameras and the relative poses of the cameras and the laserscanners have to be known. In this manuscript, we present a calibration methodology for the Unified Intrinsic and Extrinsic Calibration of a Multi-Camera-System and a Laserscanner (UCalMiCeL). The innovation of this methodology, which is an extension to the calibration of a single camera to a line laserscanner, is an unifying bundle adjustment step to ensure an optimal calibration of the entire sensor system. We use generic camera models, including pinhole, omnidirectional and fisheye cameras. For our approach, the laserscanner and each camera have to share a joint field of view, whereas the fields of view of the individual cameras may be disjoint. The calibration approach is tested with a sensor system consisting of two fisheye cameras and a line laserscanner with a range measuring accuracy of 30mm. We evaluate the estimated relative poses between the cameras quantitatively by using an additional calibration approach for Multi-Camera-Systems based on control points which are accurately measured by a motion capture system. In the experiments, our novel calibration method achieves a relative pose estimation with a deviation below 1.8• and 6.4mm.

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Indirect Correspondence-Based Robust Extrinsic Calibration of LiDAR and Camera

Cited by 36 publications

References 20 publications

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

Spatiotemporal Camera-LiDAR Calibration: A Targetless and Structureless Approach

LiDAR-IMU Time Delay Calibration Based on Iterative Closest Point and Iterated Sigma Point Kalman Filter

UCalMiCeL – UNIFIED INTRINSIC AND EXTRINSIC CALIBRATION OF A MULTI-CAMERA-SYSTEM AND A LASERSCANNER

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