Online Active Calibration for a Multi-LRF System

Xie, Guoyang; Xu, Tao; Isert, Carsten; Aeberhard, Michael; Li, Shaohua; Liu, Ming

doi:10.1109/itsc.2015.136

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…Note that the error of the resulted calibrated translation and rotation is comparatively lower than state-of-the-art approaches, even for cases with auxiliary objects [6], even with highly drifted initial conditions. Due to limited space, he full comparison results will be presented in a separate report in http://ram-lab.com/file/report_ extrinsic.pdf.…”

Section: Methodsmentioning

confidence: 92%

“…Feature association is to find the correspondences from image pixels captured by cameras to the point-cloud captured by range finders. If we have the correspondences, this problem will be further solved as a PnP problem [5], optimization problem [4] or even active calibration [6] which have been already well-studied. However, images and pointcloud are hard to be matched due to the inherent representation difference: The images captured from cameras are dense representations, for which each pixel has a proper definition.…”

Section: B Challengesmentioning

confidence: 99%

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Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

Liao

Liu

2019

2019 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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Fusion of heterogeneous extroceptive sensors is the most effient and effective way to representing the environment precisely, as it overcomes various defects of each homogeneous sensor. The rigid transformation (aka. extrinsic parameters) of heterogeneous sensory systems should be available before precisely fusing the multisensor information. Researchers have proposed several approaches to estimating the extrinsic parameters. These approaches require either auxiliary objects, like chessboards, or extra help from human to select correspondences. In this paper, we proposed a novel extrinsic calibration approach for the extrinsic calibration of range and image sensors. As far as we know, it is the first automatic approach with no requirement of auxiliary objects or any human interventions.First, we estimate the initial extrinsic parameters from the individual motion of the range finder and the camera. Then we extract lines in the image and point-cloud pairs, to refine the line feature associations by the initial extrinsic parameters.At the end, we discussed the degenerate case which may lead to the algorithm failure and validate our approach by simulation. The results indicate high-precision extrinsic calibration results against the ground-truth.

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Section: Methodsmentioning

confidence: 92%

Section: B Challengesmentioning

confidence: 99%

Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

Liao

Liu

2019

2019 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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“…For example, Zhang [8] introduced the idea of calibrating the extrinsic parameters between a camera and an LRF by using a checkerboard as a pattern. Based on this method, Xie et al [9] experimented with an autonomous vehicle that contained three LRFs and a camera. They calibrated the extrinsic parameters between three LRFs and a camera, and then calculated the extrinsic parameters between the three LRFs by using the mutual camera as a bridge.…”

Section: Introductionmentioning

confidence: 99%

Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron

Zhu

Huang

Tian

et al. 2020

Sensors

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Multiple two-dimensional laser rangefinders (LRFs) are applied in many applications like mobile robotics, autonomous vehicles, and three-dimensional reconstruction. The extrinsic calibration between LRFs is the first step to perform data fusion and practical application. In this paper, we proposed a simple method to calibrate LRFs based on a corner composed of three mutually perpendicular planes. In contrast to other methods that require a special pattern or assistance from other sensors, the trihedron corner needed in this method is common in daily environments. In practice, we can adjust the position of the LRFs to observe the corner until the laser scanning plane intersects with three planes of the corner. Then, we formed a Perspective-Three-Point problem to solve the position and orientation of each LRF at the common corner coordinate system. The method was validated with synthetic and real experiments, showing better performance than existing methods.

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Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction using an Arbitrary Planar Board

Kim

Han

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Online Active Calibration for a Multi-LRF System

Cited by 10 publications

References 17 publications

Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

Extrinsic Calibration of Multiple Two-Dimensional Laser Rangefinders Based on a Trihedron

Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction using an Arbitrary Planar Board

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