Online SLAM with any-time self-calibration and automatic change detection

Keivan, Nima; Sibley, Gabe

doi:10.1109/icra.2015.7140008

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…In addition, this approach includes a full projective camera model where the calibration is pre-computed after video capture. Again, while self-calibration has been addressed for decades in rigid shapes [12], [16], [22], [34], its incorporation in non-rigid scenarios is very limited. Table I summarizes a qualitative comparison in terms of available characteristics of our approach and the most relevant competing approaches.…”

Section: Related Workmentioning

confidence: 99%

Total Estimation from RGB Video: On-line Camera Self-Calibration, Non-Rigid Shape and Motion

Agudo

2021

2020 25th International Conference on Pattern Recognition (ICPR)

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In this paper we present a sequential approach to jointly retrieve camera auto-calibration, camera pose and the 3D reconstruction of a non-rigid object from an uncalibrated RGB image sequence, without assuming any prior information about the shape structure, nor the need for a calibration pattern, nor the use of training data at all. To this end, we propose a Bayesian filtering approach based on a sum-of-Gaussians filter composed of a bank of extended Kalman filters (EKF). For every EKF, we make use of dynamic models to estimate its state vector, which later will be Gaussianly combined to achieve a global solution.To deal with deformable objects, we incorporate a mechanical model solved by using the finite element method. Thanks to these ingredients, the resulting method is both efficient and robust to several artifacts such as missing and noisy observations as well as sudden camera motions, while being available for a wide variety of objects and materials, including isometric and elastic shape deformations. Experimental validation is proposed in real experiments, showing its strengths with respect to competing approaches.

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Section: Related Workmentioning

confidence: 99%

Total Estimation from RGB Video: On-line Camera Self-Calibration, Non-Rigid Shape and Motion

Agudo

2021

2020 25th International Conference on Pattern Recognition (ICPR)

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“…Though this contribution proposes a method to be used with a mono camera, it can be easily extended for a multi camera or multi sensor setting. 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.…”

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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“…The camera calibration is an important activity (Keivan & Sibley, 2015). In this paper, a low-cost camera (2MP and f4.8mm) was fixed on the robot to 8 cm from the floor, (Figures 1 and 10).…”

Section: A Segmentation Of Road Imagesmentioning

confidence: 99%

Generating Road Network Graph with Vision-Based Unmanned Vehicle

Yano

Zampirolli

2017

IJURCA

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This work has undergone a double-blind review by a minimum of two faculty members from institutions of higher learning from around the world. The faculty reviewers have expertise in disciplines closely related to those represented by this work. If possible, the work was also reviewed by undergraduates in collaboration with the faculty reviewers. AbstractWith the advancement of technology and its cheapness, robotic vehicles have gained a large number of applications. The spread of their use is growing also because they are getting smaller, lighter and easier to build. In this paper we present a simple and effective way to map a road network with the help of a driverless vehicle. Our approach consists of only three parts: vision-segmentation, angle variation and travelled distance. A video camera attached to a Lego® NXT Mindstorm vehicle guides it by image segmentation using Matlab® Image processing toolbox, along a road network, in which is represented by black tape over a white floor. The algorithm makes the vehicle travel all over the road memorizing main coordinates to identify all crossroads by keeping track of the travelled distance and the current angle. The crossroads and road's end are the nodes of the graph. After several simulations have been performed, the modelling proved to be successful in that small scale approach. Consequently, there are good chances that driverless cars and UAVs also make use of the strategies to map route networks accordingly. The algorithm presented in this paper is useful when there is no localization signal such as GPS, for example, navigation on water, tunnels, inside buildings, among others.

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Online SLAM with any-time self-calibration and automatic change detection

Cited by 20 publications

References 30 publications

Total Estimation from RGB Video: On-line Camera Self-Calibration, Non-Rigid Shape and Motion

Total Estimation from RGB Video: On-line Camera Self-Calibration, Non-Rigid Shape and Motion

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

Generating Road Network Graph with Vision-Based Unmanned Vehicle

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