by admin

A Multiple-Camera System Calibration Toolbox Using a Feature Descriptor-Based Calibration Pattern

July 18, 2013 in ETHZ-CVG, Publications, year 3 by admin

Bo Li, Lionel Heng, Kevin Koeser, and Marc Pollefeys

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

This paper presents a novel feature descriptorbased calibration pattern and a Matlab toolbox which uses the specially designed pattern to easily calibrate both the intrinsics and extrinsics of a multiple-camera system. In contrast to existing calibration patterns, in particular, the ubiquitous chessboard, the proposed pattern contains many more features of varying scales; such features can be easily and automatically detected. The proposed toolbox supports the calibration of a camera system which can comprise either normal pinhole cameras or catadioptric cameras. The calibration only requires that neighboring cameras observe parts of the calibration pattern at the same time; the observed parts may not overlap at all. No overlapping ???elds of view are assumed for the camera system. We show that the toolbox can easily be used to automatically calibrate camera systems.


@inproceedings{liIROS13b,
author = {Bo Li and
Lionel Heng and
Kevin Koeser and
Marc Pollefeys},
title = {A Multiple-Camera System Calibration Toolbox Using a Feature Descriptor-Based Calibration Pattern},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2013},
pages = {}
}

Article full text

by admin

A 4-Point Algorithm for Relative Pose Estimation of a Calibrated Camera with a Known Relative Rotation Angle

July 18, 2013 in ETHZ-CVG, Publications, year 3 by admin

Bo Li, Lionel Heng, Gim Hee Lee, and Marc Pollefeys

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

We propose an algorithm to estimate the relative camera pose using four feature correspondences and one relative rotation angle measurement. The algorithm can be used for relative pose estimation of a rigid body equipped with a camera and a relative rotation angle sensor which can be either an odometer, an IMU or a GPS/INS system. This algorithm exploits the fact that the relative rotation angles of both the camera and relative rotation angle sensor are the same as the camera and sensor are rigidly mounted to a rigid body. Therefore, knowledge of the extrinsic calibration between the camera and sensor is not required. We carry out a quantitative comparison of our algorithm with the well-known 5-point and 1-point algorithms, and show that our algorithm exhibits the highest level of accuracy.


@inproceedings{liIROS13a,
author = {Bo Li and
Lionel Heng and
Gim Hee Lee and
Marc Pollefeys},
title = {A 4-Point Algorithm for Relative Pose Estimation of a Calibrated Camera with a Known Relative Rotation Angle},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2013},
pages = {}
}

Article full text

by admin

CamOdoCal: Automatic Intrinsic and Extrinsic Calibration of a Rig with Multiple Generic Cameras and Odometry

July 18, 2013 in ETHZ-CVG, Publications, year 3 by admin

Lionel Heng, Bo Li, and Marc Pollefeys

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

Multiple cameras are increasingly prevalent on robotic and human-driven vehicles. These cameras come in a variety of wide-angle, fish-eye, and catadioptric models. Furthermore, wheel odometry is generally available on the vehicles on which the cameras are mounted. For robustness, vision applications tend to use wheel odometry as a strong prior for camera pose estimation, and in these cases, an accurate extrinsic calibration is required in addition to an accurate intrinsic calibration. To date, there is no known work on automatic intrinsic calibration of generic cameras, and more importantly, automatic extrinsic calibration of a rig with multiple generic cameras and odometry.

We propose an easy-to-use automated pipeline that handles both intrinsic and extrinsic calibration; we do not assume that there are overlapping fields of view. At the begining, we run an intrinsic calibration for each generic camera. The intrinsic calibration is automatic and requires a chessboard. Subsequently, we run an extrinsic calibration which finds all camera-odometry transforms. The extrinsic calibration is unsupervised, uses natural features, and only requires the vehicle to be driven around for a short time. The intrinsic parameters are optimized in a final bundle adjustment step in the extrinsic calibration. In addition, the pipeline produces a globally-consistent sparse map of landmarks which can be used for visual localization. The pipeline is publicly available as a standalone C++ package.


@inproceedings{hengIROS13,
author = {Lionel Heng and
Bo Li and
Marc Pollefeys},
title = {CamOdoCal: Automatic Intrinsic and Extrinsic Calibration of a Rig with Multiple Generic Cameras and Odometry},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2013},
pages = {}
}

Article full text

by admin

Robust Pose-Graph Loop-Closures with Expectation-Maximization

July 18, 2013 in ETHZ-CVG, Publications, year 3 by admin

Gim Hee Lee, Friedrich Fraundorfer, and Marc Pollefeys

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

In this paper, we model the robust loop-closure pose-graph SLAM problem as a Bayesian network and show that it can be solved with the Classification Expectation-Maximization (EM) algorithm.
In particular, we express our robust pose-graph SLAM as a Bayesian network where the robot poses and constraints are latent and observed variables. An additional set of latent variables is introduced as weights for the loop-constraints. We show that the weights can be chosen as the Cauchy function, which are iteratively computed from the errors between the predicted robot poses and observed loop-closure constraints in the Expectation step, and used to weigh the cost functions from the pose-graph loop-closure constraints in the Maximization step. As a result, outlier loop-closure constraints are assigned low weights and exert less influences in the pose-graph optimization within the EM iterations.
We show proofs of the conceptual similarity between our EM algorithm and the M-Estimator. Specifically, we show that the weight function in our EM algorithm is equivalent to the robust residual function in the M-Estimator. We verify our proposed algorithm with experimental results from multiple simulated and real-world datasets, and comparisons with other +existing works.


@inproceedings{IROS13_Lee_Robust,
author = {Gim Hee Lee and
Friedrich Faundorfer and
Marc Pollefeys},
title = {Robust Pose-Graph Loop-Closures with Expectation-Maximization},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2013},
pages = {}
}

Article full text

by admin

Structureless Pose-Graph Loop-Closure with a Multi-Camera System on a Self-Driving Car

July 18, 2013 in ETHZ-CVG, Publications, year 3 by admin

Gim Hee Lee, Friedrich Fraundorfer, Marc Pollefeys

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

In this paper, we propose a method to compute the pose-graph loop-closure constraints using multiple non/minimal overlapping field-of-views cameras mounted rigidly on a self-driving car without the need to reconstruct any 3D scene points.
In particular, we show that the relative pose with metric scale between two loop-closing pose-graph vertices can be directly obtained from the epipolar geometry of the multi-cameras system. As a result, we avoid the additional time complexities and uncertainties from the reconstruction of 3D scene points which are needed by standard monocular and stereo approaches. In addition, there is a greater flexibility in choosing a configuration for the multi-camera system to cover a wider field-of-view so as to avoid missing out any loop-closure opportunities.
We show that by expressing the point correspondences between two frames as Pluecker lines and enforcing the planar motion constraint on the car, we are able to use multiple cameras as one and formulate the relative pose problem for loop-closure as a minimal problem which requires 3-point correspondences that yields up to six real solutions. The RANSAC algorithm isused to determine the correct solution and for robust estimation. We verify our method with results from multiple large-scale real-world data.


@inproceedings{IROS13_Lee_Structureless,
author = {Gim Hee Lee and
Friedrich Faundorfer and
Marc Pollefeys},
title = {Structureless Pose-Graph Loop-Closure with a Multi-Camera System on a Self-Driving Car},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2013},
pages = {}
}

Article full text

by admin

Introspective Active Learning for Scalable Semantic Mapping

July 2, 2013 in Oxford-MRG, Publications, year 3 by admin

Rudolph Triebel, Hugo Grimmett, Rohan Paul, Ingmar Posner

Workshop. Robotics Science and Systems (RSS)

This paper proposes an active learning framework for semantic mapping in mobile robotics. In particular, our work explores the benefits of an introspective classifier over that of a more traditional non-introspective approach for active data selection. We extend the notion of introspection to a particular sparse Gaussian Process classifier, the Informative Vector Machine (IVM), and show that the use of an IVM leads to more informative questions being asked during active learning. We further leverage the information-theoretic nature of the IVM to formulate a principled mechanism for forgetting stale data. The result is an efficient and highly effective end-to-end active learning framework which outperforms both passive approaches as well as active approaches based on the more commonly used Support Vector Machine (SVM) in terms of classification performance and learning rate on a publicly available dataset.


@INPROCEEDINGS { TriebelRSSWorkshop2013,
AUTHOR = { Rudolph Triebel, Hugo Grimmett, Rohan Paul, Ingmar Posner },
BOOKTITLE = { Workshop. Robotics Science and Systems (RSS) },
MONTH = jun,
TITLE = {Introspective Active Learning for Scalable Semantic Mapping },
YEAR = { 2013 },
}

by admin

A Sampling-Based Partial Motion Planning Framework for System-Compliant Navigation along a Reference Path

July 1, 2013 in ETHZ-ASL, Publications by admin

Ulrich Schwesinger, Martin Rufli , Paul Furgale, and Roland Siegwart

IEEE Intelligent Vehicles Symposium (IVS) 2013

In this paper a generic framework for sampling-based partial motion planning along a reference path is presented. The sampling mechanism builds on the specification of a vehicle model and a control law, both of which are freely selectable. Via a closed-loop forward simulation, the vehicle model is regulated onto a carefully chosen set of terminal states aligned with the reference path, generating system-compliant sample trajectories in accordance with the specified system and environmental constraints. The consideration of arbitrary state and input limits make this framework appealing to nonholonomic systems. The rich trajectory set is evaluated in an online sampling-based planning framework, targeting real-time motion planning in dynamic environments.

In an example application, a Volkswagen Golf is modeled via a kinodynamic single-track system that is further constrained by steering angle/rate and velocity/acceleration limits. Control is implemented via state-feedback onto piecewise C0 -continuous reference paths. Experiments demonstrate the planner’s applicability to online operation, its ability to cope with discontinuous reference paths as well as its capability to navigate in a realistic traffic scenario.


@inproceedings{ schwesinger_iv13,
Address = {Gold Coast, Australia},
Author = {Schwesinger, Ulrich and Rufli, Martin and Furgale, Paul and Siegwart, Roland},
Booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
Month = {23--26 June},
Pages = {391--396},
Title = {A Sampling-Based Partial Motion Planning Framework for System-Compliant Navigation along a Reference Path},
Year = {2013}
}

Article full text

by admin

Self-supervised Calibration for Robotic Systems

July 1, 2013 in ETHZ-ASL, Publications by admin

Jérôme Maye, Paul Furgale, and Roland Siegwart

IEEE Intelligent Vehicles Symposium (IVS) 2013

We present a generic algorithm for self calibration of robotic systems that utilizes two key innovations. First, it uses information theoretic measures to automatically identify and store novel measurement sequences. This keeps the computation tractable by discarding redundant information and allows the system to build a sparse but complete calibration dataset from data collected at different times. Second, as the full observability of the calibration parameters may not be guaranteed for an arbitrary measurement sequence, the algorithm detects and locks unobservable directions in parameter space using a truncated QR decomposition of the Gauss-Newton system. The result is an algorithm that listens to an incoming sensor stream, builds a minimal set of data for estimating the calibration parameters, and updates parameters as they become observable, leaving the others locked at their initial guess. Through an extensive set of simulated and real-world experiments, we demonstrate that our method outperforms state-of-the-art algorithms in terms of stability, accuracy, and computational efficiency.


@inproceedings{maye_iv13,
Address = {Gold Coast, Australia},
Author = {Maye, Jerome and Furgale, Paul and Siegwart, Roland},
Booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
Month = {23--26 June},
Pages = {473--480},
Title = {Self-supervised Calibration for Robotic Systems},
Year = {2013}
}

Article full text

by admin

Rolling Shutter Camera Calibration

July 1, 2013 in ETHZ-ASL, Publications by admin

Luc Oth, Paul Furgale, Laurent Kneip, Roland Siegwart

2013 IEEE Conference on Computer Vision and Pattern Recognition

Rolling Shutter (RS) cameras are used across a wide range of consumer electronic devices—from smart-phones to high-end cameras. It is well known, that if a RS camera is used with a moving camera or scene, significant image distortions are introduced. The quality or even success of structure from motion on rolling shutter images requires the usual intrinsic parameters such as focal length and distortion coefficients as well as accurate modelling of the shutter timing.

The current state-of-the-art technique for calibrating the shutter timings requires specialised hardware. We present a new method that only requires video of a known calibration pattern. Experimental results on over 60 real datasets show that our method is more accurate than the current state of the art.


@inproceedings{oth_cvpr13,
Author = {Luc Oth and Paul Furgale and Laurent Kneip and Roland Siegwart},
Booktitle = {Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on},
Month = {25--27 June},
Title = {Rolling Shutter Camera Calibration},
Year = {2013}
}

Article full text

by admin

Toward Automated Driving in Cities using Close-to-Market Sensors, an Overview of the V-Charge Project

July 1, 2013 in Bosch, ETHZ-ASL, ETHZ-CVG, Oxford-MRG, Parma-Vislab, Publications, TUB, VW by admin

Paul Furgale et al.

IEEE Intelligent Vehicles Symposium (IVS) 2013

Future requirements for drastic reduction of CO2 production and energy consumption will lead to significant changes in the way we see mobility in the years to come.

However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times. Automated cars have the potential to reduce the environmental impact of driving, and increase the safety of motor vehicle travel. The current state-of-the-art in vehicle automation requires a suite of expensive sensors. While the cost of these sensors is decreasing, integrating them into electric cars will increase the price and represent another barrier to adoption.

The V-Charge Project, funded by the European Commission, seeks to address these problems simultaneously by developing an electric automated car, outfitted with close-to-market sensors, which is able to automate valet parking and recharging for integration into a future transportation system. The final goal is the demonstration of a fully operational system including automated navigation and parking.

This paper presents an overview of the V-Charge system, from the platform setup to the mapping, perception, and planning sub-systems.


@inproceedings{furgale_iv13,
Address = {Gold Coast, Australia},
Annote = {full-conf-pb},
Author = {Paul Furgale and Ulrich Schwesinger and Martin Rufli and Wojciech Derendarz and Hugo Grimmett and Peter M\"{u}hlfellner and Stefan Wonneberger and Julian Timpner Stephan Rottmann and Bo Li and Bastian Schmidt and Thien Nghia Nguyen and Elena Cardarelli and Stefano Cattani and Stefan Br\"{u}ning and Sven Horstmann and Martin Stellmacher and Holger Mielenz and Kevin K\"{o}ser and Markus Beermann and Christian H\"{a}ne and Lionel Heng and Gim Hee Lee and Friedrich Fraundorfer and Ren\'{e} Iser and Rudolph Triebel and Ingmar Posner and Paul Newman and Lars Wolf and Marc Pollefeys and Stefan Brosig and Jan Effertz and C\'edric Pradalier and Roland Siegwart},
Booktitle = {IEEE Intelligent Vehicles Symposium (IV)},
Month = {23--26 June},
Pages = {809--816},
Title = {{Toward Automated Driving in Cities using Close-to-Market Sensors, an Overview of the V-Charge Project}},
Year = {2013}
}

Article full text