Hand-eye autocalibration of camera positions on vehicles

Ruland, Thomas; Loose, Heidi; Pajdla, Tomáš; Krüger, Lars

doi:10.1109/itsc.2010.5625279

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…This can be explained by the change in height and orientation with respect to the ground plane during turns. Ruland et al [15] also observed a strong dependency between errors in camera height and estimation errors. Both approaches appear to be very sensitive to nonplanarities, as can be seen in the last row in both tables respectively.…”

Section: Quantitative Evaluationmentioning

confidence: 93%

“…Hence, the decoupled estimation of the relative orientation, such as in [6], based on rotations only, is not possible. The work of Ruland et al [15] is dedicated to this case. They use the Ackermann steering model and given motion parameters to estimate the 2D position of a camera with respect to the vehicle coordinate frame.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Online extrinsic multi-camera calibration using ground plane induced homographies

Knorr

Niehsen

Stiller

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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This paper presents an approach for online estimation of the extrinsic calibration parameters of a multicamera rig. Given a coarse initial estimate of the parameters, the relative poses between cameras are refined through recursive filtering. The approach is purely vision based and relies on plane induced homographies between successive frames. Overlapping fields of view are not required. Instead, the ground plane serves as a natural reference object. In contrast to other approaches, motion, relative camera poses, and the ground plane are estimated simultaneously using a single iterated extended Kalman filter. This reduces not only the number of parameters but also the computational complexity. Furthermore, an arbitrary number of cameras can be incorporated. Several experiments on synthetic as well as real data were conducted using a setup of four synchronized wide angle fisheye cameras, mounted on a moving platform. Results were obtained, using both, a planar and a general motion model with full six degrees of freedom. Additionally, the effects of uncertain intrinsic parameters and nonplanar ground were evaluated experimentally.

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Section: Quantitative Evaluationmentioning

confidence: 93%

Section: Introduction and Related Workmentioning

confidence: 99%

Online extrinsic multi-camera calibration using ground plane induced homographies

Knorr

Niehsen

Stiller

2013

2013 IEEE Intelligent Vehicles Symposium (IV)

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“…Approaches for automotive camera self-calibration are either aiming at intrinsic calibration (Houben, 2014), (Keivan and Sibley, 2015) , i.e. estimating the interior orientation and distortion parameters of the cameras, at extrinsic calibration (Ruland et al, 2010), (Heng et al, 2014), i.e. estimating the parameters of the exterior orientation, or at both (Heng et al, 2013).…”

Section: Automotive Camera Self-calibrationmentioning

confidence: 99%

“…In contrast, approaches relying on image features detected by descriptors like SIFT (Dang et al, 2009) or SURF (Heng et al, 2014) are typically not subject to the aforementioned constraints. Though, robustness might be hard to achieve by a SfM method for camera self-calibration due to problems with correspondence search in road scene images, as reported by (Ruland et al, 2010) for the cases of poor illumination or poor road textures. Additionally it has been reported that moving cars or pedestrians could have negative influence on camera calibration, wherefore some methods include special outlier removal steps to make calibration more robust, like (Dang et al, 2009).…”

Section: Automotive Camera Self-calibrationmentioning

confidence: 99%

Semantic Road Scene Knowledge for Robust Self-Calibration of Environment-Observing Vehicle Cameras

Hanel

Stilla

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Environment-observing vehicle camera self-calibration using a structure from motion (SfM) algorithm allows calibration over vehicle lifetime without the need of special calibration objects being present in the calibration images. Scene-specific problems with featurebased correspondence search and reconstruction during the SfM pipeline might be caused by critical objects like moving objects, poor-texture objects or reflecting objects and might have negative influence on camera calibration. In this contribution, a method to use semantic road scene knowledge by means of semantic masks for a semantic-guided SfM algorithm is proposed to make the calibration more robust. Semantic masks are used to exclude image parts showing critical objects from feature extraction, whereby semantic knowledge is obtained by semantic segmentation of the road scene images. The proposed method is tested with an image sequence recorded in a suburban road scene. It has been shown that semantic guidance leads to smaller deviations of the estimated interior orientation and distortion parameters from reference values obtained by test field calibration compared to a standard SfM algorithm.

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“…Intrinsic camera parameters include focal length and pixel size, while extrinsic parameters describe camera position and rotation relative to the vehicle body. Both can be obtained by either manual calibration using a predefined calibration pattern or automatically by means of online calibration algorithms [24].…”

Section: A System Descriptionmentioning

confidence: 99%

Surround view pedestrian detection using heterogeneous classifier cascades

Gressmann

Palm

Lohlein

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Pedestrian detection is of particular interest to the automotive domain, where an accurate estimation of a pedestrian's position is the first step towards reliable collision avoidance systems. Driven by rapid advances in technology, several systems to detect pedestrians in front of a moving vehicle have been proposed in recent years. This paper introduces a novel pedestrian detection system for low-speed driving scenarios, capable of detecting pedestrians in a 360-degree fashion around the vehicle. Detected pedestrians are displayed to the driver in an intuitive way using a dynamically generated Birds's Eye View image. Furthermore, a novel classifier architecture to efficiently handle this complex application scenario is provided. By combining the processing speed of a classifier cascade with the discriminative power of a multi-stage neural network, the system achieves state of the art performance while retaining real-time capability. To keep classifier complexity low, a new feature-based inter-stage information transfer method is presented. All classifier components are compared to recent pedestrian detection approaches and evaluated on a real-world data set.

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Hand-eye autocalibration of camera positions on vehicles

Cited by 11 publications

References 12 publications

Online extrinsic multi-camera calibration using ground plane induced homographies

Online extrinsic multi-camera calibration using ground plane induced homographies

Semantic Road Scene Knowledge for Robust Self-Calibration of Environment-Observing Vehicle Cameras

Surround view pedestrian detection using heterogeneous classifier cascades

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