Non-parametric calibration for depth sensors

Cicco, Maurilio Di; Iocchi, Luca; Grisetti, Giorgio

doi:10.1016/j.robot.2015.08.004

Cited by 17 publications

(20 citation statements)

References 15 publications

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“…This sequence, as well as a set of infrared and color frame pairs viewing the checkerboard, are uploaded by the user as input to the calibration. Our system then runs a calibration procedure based on [84,14] to obtain intrinsic parameters for both depth and color sensors, and an extrinsic transformation of depth to color. We find that this calibration procedure is easy for users and results in improved data and consequently enhanced reconstruction quality.…”

Section: Rgb-d Scanningmentioning

confidence: 99%

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

Dai

Chang

Savva

et al. 2017

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

2,818

2,478

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A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available -current datasets cover a small range of scene views and have limited semantic annotations. To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction and crowdsourced semantic annotation. We show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval.

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Section: Rgb-d Scanningmentioning

confidence: 99%

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

Dai

Chang

Savva

et al. 2017

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

2,818

2,478

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“…All above-mentioned works require a visual pattern (typically a checkerboard) in order to compute reference depth measurements, and must be included in the robot workspace to enable automatic calibration. There are works not requiring this, like [18], where the authors proposed a non-parametric calibration approach and they get rid of the visual pattern requirement. However, to perform the calibration on a mobile robot, another sensor (e.g.…”

Section: Related Workmentioning

confidence: 99%

Intrinsic Calibration of Depth Cameras for Mobile Robots Using a Radial Laser Scanner

Zuñiga-Noël

Ruiz-Sarmiento

González-Jiménez

2019

Computer Analysis of Images and Patterns

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Depth cameras, typically in RGB-D configurations, are common devices in mobile robotic platforms given their appealing features: high frequency and resolution, low price and power requirements, among others. These sensors may come with significant, non-linear errors in the depth measurements that jeopardize robot tasks, like free-space detection, environment reconstruction or visual robot-human interaction. This paper presents a method to calibrate such systematic errors with the help of a second, more precise range sensor, in our case a radial laser scanner. In contrast to what it may seem at first, this does not mean a serious limitation in practice since these two sensors are often mounted jointly in many mobile robotic platforms, as they complement well each other. Moreover, the laser scanner can be used just for the calibration process and get rid of it after that. The main contributions of the paper are: i) the calibration is formulated from a probabilistic perspective through a Maximum Likelihood Estimation problem, and ii) the proposed method can be easily executed automatically by mobile robotic platforms. To validate the proposed approach we evaluated for both, local distortion of 3D planar reconstructions and global shifts in the measurements, obtaining considerably more accurate results. A C++ open-source implementation of the presented method has been released for the benefit of the community.

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“…Recently, Di Cicco et al [32] proposed a non-parametric, unsupervised intrinsics calibration method for depth sensors. The best plane fitted to the data is used as a reference, i.e., the average depth is considered reliable.…”

Section: Related Workmentioning

confidence: 99%

“…To model the effects of the errors introduced by depth sensors, as in [25], [29], [30], [32], we propose to estimate a depth correction function in a per-pixel basis. That is, given a depth sensor D that provides a depth image I D of size H D × W D , a pixel (u, v) T and the corresponding depth value z = I D (u, v), the real depth z * is computed as:…”

Section: A Error Correction Modelmentioning

confidence: 99%

See 1 more Smart Citation

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

2018

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Color-depth cameras (RGB-D cameras) have become the primary sensors in most robotics systems, from service robotics to industrial robotics applications. Typical consumergrade RGB-D cameras are provided with a coarse intrinsic and extrinsic calibration that generally does not meet the accuracy requirements needed by many robotics applications (e.g., highly accurate 3D environment reconstruction and mapping, high precision object recognition and localization, . . . ). In this paper, we propose a human-friendly, reliable and accurate calibration framework that enables to easily estimate both the intrinsic and extrinsic parameters of a general color-depth sensor couple. Our approach is based on a novel two components error model. This model unifies the error sources of RGB-D pairs based on different technologies, such as structured-light 3D cameras and time-of-flight cameras. Our method provides some important advantages compared to other state-of-the-art systems: it is general (i.e., well suited for different types of sensors), based on an easy and stable calibration protocol, provides a greater calibration accuracy, and has been implemented within the ROS robotics framework. We report detailed experimental validations and performance comparisons to support our statements.

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Non-parametric calibration for depth sensors

Cited by 17 publications

References 15 publications

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

Intrinsic Calibration of Depth Cameras for Mobile Robots Using a Radial Laser Scanner

Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras

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