Online calibration of stereo rigs for long-term autonomy

Warren, Michael; McKinnon, David; Upcroft, Ben

doi:10.1109/icra.2013.6631096

Cited by 40 publications

(31 citation statements)

References 9 publications

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“…We chose to include Z. Zhang's checkerboard calibration algorithm [2] in our evaluation because it serves the purpose of our research and is a standard method for this calibration class [9]. The AMCC toolbox [11] (an automation wrapper for Bouguet's Matlab toolbox [6] of Zhang's algorithm [2]) implementation was selected for evaluation because it fully automates the checkerboard corner identification.…”

Section: Selection Of Calibration Methodsmentioning

confidence: 99%

Assessment of multi-camera calibration algorithms for two-dimensional camera arrays relative to ground truth position and direction

Dima

Sjöström

Olsson

2016

2016 3dtv-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3dtv-Con)

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Camera calibration methods are commonly evaluated on cumulative reprojection error metrics, on disparate one-dimensional datasets. To evaluate calibration of cameras in two-dimensional arrays, assessments need to be made on two-dimensional datasets with constraints on camera parameters. In this study, accuracy of several multi-camera calibration methods has been evaluated on camera parameters that are affecting view projection the most. As input data, we used a 15-viewpoint two-dimensional dataset with intrinsic and extrinsic parameter constraints and extrinsic ground truth. The assessment showed that self-calibration methods using structure-from-motion reach equal intrinsic and extrinsic parameter estimation accuracy with standard checkerboard calibration algorithm, and surpass a well-known self-calibration toolbox, BlueCCal. These results show that self-calibration is a viable approach to calibrating two-dimensional camera arrays, but improvements to state-of-art multi-camera feature matching are necessary to make BlueCCal as accurate as other self-calibration methods for two-dimensional camera arrays.

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Section: Selection Of Calibration Methodsmentioning

confidence: 99%

Assessment of multi-camera calibration algorithms for two-dimensional camera arrays relative to ground truth position and direction

Dima

Sjöström

Olsson

2016

2016 3dtv-Conference: The True Vision - Capture, Transmission and Display of 3D Video (3dtv-Con)

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“…This leads to a total of 6n+6+3m parameters with which to optimize: 6 for each base camera, 6 for the stereo transform of the secondary camera and 3 for each scene point. We leave the details to a separate paper [31].…”

Section: A Constrained Bundle Adjustment 1) Stereo Bundle Adjustmentmentioning

confidence: 99%

“…• VO with constrained stereo optimization • VO with unconstrained stereo optimization (see [31]) 1) Results: Figures 4 and 5 show the simulated results. Figure 4 shows the variation of the stereo transform for the two separate experiments by subtracting the original parameter value from the estimate to show the difference.…”

Section: A Simulated Experimentsmentioning

confidence: 99%

High Altitude Stereo Visual Odometry

Warren

Upcroft

2013

Robotics: Science and Systems IX

Self Cite

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Abstract-Stereo visual odometry has received little investigation in high altitude applications due to the generally poor performance of rigid stereo rigs at extremely small baseline-to-depth ratios. Without additional sensing, metric scale is considered lost and odometry is seen as effective only for monocular perspectives. This paper presents a novel modification to stereo based visual odometry that allows accurate, metric pose estimation from high altitudes, even in the presence of poor calibration and without additional sensor inputs. By relaxing the (typically fixed) stereo transform during bundle adjustment and reducing the dependence on the fixed geometry for triangulation, metrically scaled visual odometry can be obtained in situations where high altitude and structural deformation from vibration would cause traditional algorithms to fail. This is achieved through the use of a novel constrained bundle adjustment routine and accurately scaled pose initializer. We present visual odometry results demonstrating the technique on a short-baseline stereo pair inside a fixed-wing UAV flying at significant height (∼30-100m).

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“…The presence of multiple types of sensors determines highly accurate intrinsics for each sensor as well as relative transformations between pairs of sensors. Researchers have extensively studied this problem for both single and multiple 2D cameras and have recently explored it for single and multiple RGB-D sensors [25], [26], [27], [28], [29], [30], [31]. Typical approaches involve first calibrating each sensor individually to compute its intrinsics, computing stereo pairs between sensors to estimate each sensor's extrinsics, and then running a joint optimization procedure to refine each sensor's intrinsics and extrinsics.…”

Section: B Data Collectionmentioning

confidence: 99%

BigBIRD: A large-scale 3D database of object instances

Singh

Sha

Narayan

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

274

207

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Abstract-The state of the art in computer vision has rapidly advanced over the past decade largely aided by shared image datasets. However, most of these datasets tend to consist of assorted collections of images from the web that do not include 3D information or pose information. Furthermore, they target the problem of object category recognition-whereas solving the problem of object instance recognition might be sufficient for many robotic tasks. To address these issues, we present a highquality, large-scale dataset of 3D object instances, with accurate calibration information for every image. We anticipate that "solving" this dataset will effectively remove many perceptionrelated problems for mobile, sensing-based robots.The contributions of this work consist of: (1) BigBIRD, a dataset of 100 objects (and growing), composed of, for each object, 600 3D point clouds and 600 high-resolution (12 MP) images spanning all views, (2) a method for jointly calibrating a multi-camera system, (3) details of our data collection system, which collects all required data for a single object in under 6 minutes with minimal human effort, and (4) multiple software components (made available in open source), used to automate multi-sensor calibration and the data collection process. All code and data are available at

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Online calibration of stereo rigs for long-term autonomy

Cited by 40 publications

References 9 publications

Assessment of multi-camera calibration algorithms for two-dimensional camera arrays relative to ground truth position and direction

Assessment of multi-camera calibration algorithms for two-dimensional camera arrays relative to ground truth position and direction

High Altitude Stereo Visual Odometry

BigBIRD: A large-scale 3D database of object instances

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