Extrinsic calibration of a 2d laser-rangefinder and a camera based on scene corners

Gomez-Ojeda, Ruben; Briales, Jesús; Fernández-Moral, Eduardo; González-Jiménez, Javier

doi:10.1109/icra.2015.7139700

Cited by 63 publications

(45 citation statements)

References 12 publications

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“…With regard to recent publications which use scene corners instead of calibration objects consisting of multiple connected planes to estimate the relative pose of a single camera and a laserscanner (Gomez-Ojeda et al, 2015), our approach has potential to be extended to a self calibration method for a sensor system consisting of multiple cameras and line laserscanners. …”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…point-toplane [14] or plane-to-plane [45,46]. Multimodal correspondences play also an important role in the extrinsic calibration of sensors of different nature, such as camera and lidar [51,49,16,18]. Numerous state-of-the-art SLAM (Simultaneous Localization and Mapping) solutions work with models that include plane and line primitives too [2,47,17,43,28,19].…”

Section: Multimodal Registrationmentioning

confidence: 99%

Convex Global 3D Registration with Lagrangian Duality

Briales

González-Jiménez

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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138

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The registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. This problem is non-convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. This paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). This formulation renders the optimization problem independent of both the number and nature of the correspondences.The main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory.Thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.

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“…In the general case where our relaxation is not tight (d ≤ f ML ), the nullspace relation (9) does not hold and there is no way to recover the optimal solution of the original problem [P] in a certifiable way 4 . However, to recover a good initial guess X 0 ∈ D P for [P] we apply a similar heuristic to Carlone et al in [16, Sec.…”

Section: Recovery: From Dual Solution To Mle Guessmentioning

confidence: 99%

“…PGO is also widely known as SE(d)-Synchronization, and it is a pervasive problem in many other fields, including computer vision and control, where closely related problems appear in the tasks of structure from motion [2], [3], extrinsic sensor calibration [4], [5], sensor network localization [6], etc.…”

Section: Introductionmentioning

confidence: 99%

Initialization of 3D pose graph optimization using Lagrangian duality

Briales

González-Jiménez

2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Pose Graph Optimization (PGO) is the de facto choice to solve the trajectory of an agent in Simultaneous Localization and Mapping (SLAM). The Maximum Likelihood Estimation (MLE) for PGO is a non-convex problem for which no known technique is able to guarantee a globally optimal solution under general conditions. In recent years, Lagrangian duality has proved suitable to provide good, frequently tight relaxations of the hard PGO problem through convex Semidefinite Programming (SDP). In this work, we build from the state-ofthe-art Lagrangian relaxation [1] and contribute a complete recovery procedure that, given the (tractable) optimal solution of the relaxation, provides either the optimal MLE solution if the relaxation is tight, or a remarkably good feasible guess if the relaxation is non-tight, which occurs in specially challenging PGO problems (very noisy observations, low graph connectivity, etc.). In the latter case, when used for initialization of local iterative methods, our approach outperforms other state-ofthe-art approaches converging to better solutions. We support our claims with extensive experiments.

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Extrinsic calibration of a 2d laser-rangefinder and a camera based on scene corners

Cited by 63 publications

References 12 publications

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

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

Convex Global 3D Registration with Lagrangian Duality

Initialization of 3D pose graph optimization using Lagrangian duality

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