A General Approach to the Extrinsic Calibration of Intelligent Vehicles Using ROS

Oliveira, Miguel; Castro, Afonso; Madeira, Tiago; Dias, Paulo; Santos, Vítor

doi:10.1007/978-3-030-35990-4_17

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…It also provides structure for a systematic approach to this kind of problem. We have already made use of these tools in other projects that require an optimization to be performed, such as the calibration of a set of sensor in an autonomous vehicle [65] and colour consistency correction in 3D reconstructions.…”

Section: Discussionmentioning

confidence: 99%

Enhancement of RGB-D Image Alignment Using Fiducial Markers

Madeira

Oliveira

Dias

2020

Sensors

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Three-dimensional (3D) reconstruction methods generate a 3D textured model from the combination of data from several captures. As such, the geometrical transformations between these captures are required. The process of computing or refining these transformations is referred to as alignment. It is often a difficult problem to handle, in particular due to a lack of accuracy in the matching of features. We propose an optimization framework that takes advantage of fiducial markers placed in the scene. Since these markers are robustly detected, the problem of incorrect matching of features is overcome. The proposed procedure is capable of enhancing the 3D models created using consumer level RGB-D hand-held cameras, reducing visual artefacts caused by misalignments. One problem inherent to this solution is that the scene is polluted by the markers. Therefore, a tool was developed to allow their removal from the texture of the scene. Results show that our optimization framework is able to significantly reduce alignment errors between captures, which results in visually appealing reconstructions. Furthermore, the markers used to enhance the alignment are seamlessly removed from the final model texture.

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Section: Discussionmentioning

confidence: 99%

Enhancement of RGB-D Image Alignment Using Fiducial Markers

Madeira

Oliveira

Dias

2020

Sensors

Self Cite

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“…As such, these previous works presented a methodology based on atomic transformations for multi-sensor, multi-modal robotic systems (Guindel et al, 2017b;Oliveira et al, 2020a,b). In this work, we extend our framework -Atomic Transformation Optimization Method (ATOM) 1 (Oliveira et al, 2020a) to also consider the calibration of 3D LiDARs along with the other supported modalities. Atomic transformations are geometric transformations that are not aggregated, i.e., they are indivisible.…”

Section: Introductionmentioning

confidence: 99%

A Camera to LiDAR calibration approach through the optimization of atomic transformations

Aguiar

Oliveira²,

Pedrosa³

et al. 2021

Expert Systems with Applications

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“…In this field, there are two main approaches, depending on which sensor the LRF is calibrated to. The first and the most studied method is the calibration of the LRF relative to a camera (Chen et al , 2016; Vasconcelos et al , 2012; Fan et al , 2019), or cameras (Häselich et al , 2012; Oliveira et al , 2020). In this method, the static transformation between the two sensors relies on finding correspondences between visual features captured by the camera and geometric features captured by the LRF.…”

Section: Introductionmentioning

confidence: 99%

2D lidar to kinematic chain calibration using planar features of indoor scenes

Lourenço

Madeira

Dias

et al. 2020

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Purpose 2D laser rangefinders (LRFs) are commonly used sensors in the field of robotics, as they provide accurate range measurements with high angular resolution. These sensors can be coupled with mechanical units which, by granting an additional degree of freedom to the movement of the LRF, enable the 3D perception of a scene. To be successful, this reconstruction procedure requires to evaluate with high accuracy the extrinsic transformation between the LRF and the motorized system. Design/methodology/approach In this work, a calibration procedure is proposed to evaluate this transformation. The method does not require a predefined marker (commonly used despite its numerous disadvantages), as it uses planar features in the point acquired clouds. Findings Qualitative inspections show that the proposed method reduces artifacts significantly, which typically appear in point clouds because of inaccurate calibrations. Furthermore, quantitative results and comparisons with a high-resolution 3D scanner demonstrate that the calibrated point cloud represents the geometries present in the scene with much higher accuracy than with the un-calibrated point cloud. Practical implications The last key point of this work is the comparison of two laser scanners: the lemonbot (authors’) and a commercial FARO scanner. Despite being almost ten times cheaper, the laser scanner was able to achieve similar results in terms of geometric accuracy. Originality/value This work describes a novel calibration technique that is easy to implement and is able to achieve accurate results. One of its key features is the use of planes to calibrate the extrinsic transformation.

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A General Approach to the Extrinsic Calibration of Intelligent Vehicles Using ROS

Cited by 7 publications

References 22 publications

Enhancement of RGB-D Image Alignment Using Fiducial Markers

Enhancement of RGB-D Image Alignment Using Fiducial Markers

A Camera to LiDAR calibration approach through the optimization of atomic transformations

2D lidar to kinematic chain calibration using planar features of indoor scenes

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