Pixels and 3-D Points Alignment Method for the Fusion of Camera and LiDAR Data

Xie, Shichao; Yang, Daoguo; Jiang, Kun; Zhong, Yuanxin

doi:10.1109/tim.2018.2879705

Cited by 59 publications

(21 citation statements)

References 14 publications

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“…Proposed method 1.8312 Supervised learning calibration [17] 0.8062 Mutual Information calibration [8] 2.8018 Xie [28] proposed method 2.9700 Trapezoid checkerboard calibration [15] 3.5691 Double parallel plate calibration [29] 4.9651…”

Section: Calibration Methods Reprojection Errors/(pixel)mentioning

confidence: 99%

A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

Cai

Weisong

Chen

et al. 2020

Sensors

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Aiming at the problems of feature point calibration method of 3D light detection and ranging (LiDAR) and camera calibration that are calibration boards in various forms, incomplete information extraction methods and large calibration errors, a novel calibration board with local gradient depth information and main plane square corner information (BWDC) was designed. In addition, the "three-step fitting interpolation method" was proposed to select feature points and obtain the corresponding coordinates of feature points in the LiDAR coordinate system and camera pixel coordinate system based on BWDC. Finally, calibration experiments were carried out, and the calibration results were verified by methods such as incremental verification and reprojection error comparison. The calibration results show that using BWDC and the "three-step fitting interpolation method" can solve quite accurate coordinate transformation matrix and intrinsic and external parameters of sensors, which dynamically change within 0.2% in the repeatable experiments. The difference between the experimental value and the actual value in the incremental verification experiment is about 0.5%. The average reprojection error is 1.8312 pixels, and the value changes at different distances do not exceed 0.1 pixels, which also show that the calibration method is accurate and stable.

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Section: Calibration Methods Reprojection Errors/(pixel)mentioning

confidence: 99%

A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

Cai

Weisong

Chen

et al. 2020

Sensors

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show abstract

“…There are two main trends in image-to-range localization: the geometry based feature matching methods [7,8] and datadriven based visual localization methods [9,1]. Geometry features are usually carefully designed to bridge the description gap between image and point cloud domains and maintain the geometry's consistent nature.…”

Section: Related Workmentioning

confidence: 99%

i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions

Yin¹,

Xu²,

Ji³

et al. 2021

Robotics: Science and Systems XVII

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We present a method for localizing a single camera with respect to a point cloud map in indoor and outdoor scenes. The problem is challenging because correspondences of local invariant features are inconsistent across the domains between image and 3D. The problem is even more challenging as the method must handle various environmental conditions such as illumination, weather, and seasonal changes. Our method can match equirectangular images to the 3D range projections by extracting cross-domain symmetric place descriptors. Our key insight is to retain condition-invariant 3D geometry features from limited data samples while eliminating the condition-related features by a designed Generative Adversarial Network. Based on such features, we further design a spherical convolution network to learn viewpoint-invariant symmetric place descriptors. We evaluate our method on extensive self-collected datasets, which involve Long-term (variant appearance conditions), Large-scale (up to 2km structure/unstructured environment), and Multistory (four-floor confined space). Our method surpasses other current state-of-the-arts by achieving around 3 times higher place retrievals to inconsistent environments, and above 3 times accuracy on online localization. To highlight our method's generalization capabilities, we also evaluate the recognition across different datasets. With a single trained model, i3dLoc can demonstrate reliable visual localization in random conditions.

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“…We consider the proposed strategy the pixel and 3-D point arrangement (PPA) technique. Guiping Jiang et al, (2019) [9] proposed, which contains two segments. Initially, the parallel sweep lines are set up along which the GPR is moved to get the B-filter pictures; the hyperbolic shapes on these acquired pictures are recognized and fitted; and the areas and profundities of the identified example purposes of the covered links could be gotten from these hyperbolas.…”

Section: Previous Workmentioning

confidence: 99%

Forgery Detection Based on KNN Classifier using SURF Feature Extraction

DHIVYA¹,

SUDHAKAR²

2019

IJRTE

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Copy move forgery is a standout amongst the most widely recognized for controlling unique pictures. Edge detection is a standout amongst the most contemplated issues in PC vision, yet it remains a difficult undertaking. It is troublesome since frequently the choice for an edge can't be made absolutely dependent on low dimension signs, for example, slope, rather we have to connect all dimensions of data, low, center, and high, so as to choose where to put edges. In this paper we propose a novel adjusted K-derives gathering calculation for edge and thing limit affirmation which we suggest as Speeded-Up Robust Features (SURF).A choice of an edge point is made autonomously at every area in the picture; an extremely huge opening is utilized giving critical setting to every choice. In the coordinating stage, the calculation chooses and consolidates an enormous number of highlights crosswise over various scales so as to gain proficiency with a discriminative model utilizing an all-inclusive rendition of the Putatively Matched Points (Including Outliers) calculation.The coordinating based structure is exceptionally versatile and there are no parameters to tune. The proposed work is pertinent to applications for imitation discovery in various explicit picture areas just as on common pictures. The outcomes are reproduced through the MatlabR2014b programming.

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Pixels and 3-D Points Alignment Method for the Fusion of Camera and LiDAR Data

Cited by 59 publications

References 14 publications

A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

A Novel Calibration Board and Experiments for 3D LiDAR and Camera Calibration

i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions

Forgery Detection Based on KNN Classifier using SURF Feature Extraction

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