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 = {}
}


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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 = {}
}


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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 = {}
}


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Christian Haene, Christopher Zach, Bernhard Zeisl, and Marc Pollefeys

2012 2nd International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission

Dense 3D reconstruction in man-made environments has to contend with weak and ambiguous observations due to texture-less surfaces which are predominant in such environments. This challenging task calls for strong, domain-specific priors. These are usually modeled via regularization or smoothness assumptions. Generic smoothness priors, e.g. total variation are often not sufficient to produce convincing results. Consequently, we propose a more powerful prior directly modeling the expected local surface-structure, without the need to utilize expensive methods such as higher-order MRFs. Our approach is inspired by patch-based representations used in image processing. In contrast to the over-complete dictionaries used e.g. for sparse representations our patch dictionary is much smaller. The proposed energy can be optimized by utilizing an efficient first-order primal dual algorithm. Our formulation is in particular very natural to model priors on the 3D structure of man-made environments. We demonstrate the applicability of our prior on synthetic data and on real data, where we recover dense, piece-wise planar 3D models using stereo and fusion of multiple depth images.


@inproceedings{haene3DIMPVT13,
author = {Christian Haene and Christopher Zach and Bernhard Zeisl and Marc Pollefeys},
title = {A Patch Prior for Dense 3D Reconstruction in Man-Made Environments},
booktitle = {3DIMPVT},
year = {2013},
pages = {}
}

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