Computation with infinite neural networks
Neural Computation
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Real-Time Simultaneous Localisation and Mapping with a Single Camera
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Exactly Sparse Extended Information Filters for Feature-based SLAM
International Journal of Robotics Research
Vision-Based SLAM: Stereo and Monocular Approaches
International Journal of Computer Vision
Gaussian process modeling of large scale terrain
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Contextual occupancy maps using Gaussian processes
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Appearance-Guided Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles
IEEE Transactions on Robotics
Artificial Intelligence in Medicine
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This paper addresses the visual odometry problem from a machine learning perspective. Optical flow information from a single camera is used as input for a multiple-output Gaussian process (MOGP) framework, that estimates linear and angular camera velocities. This approach has several benefits. (1) It substitutes the need for conventional camera calibration, by introducing a semi-parametric model that is able to capture nuances that a strictly parametric geometric model struggles with. (2) It is able to recover absolute scale if a range sensor (e.g. a laser scanner) is used for ground-truth, provided that training and testing data share a certain similarity. (3) It is naturally able to provide measurement uncertainties. We extend the standard MOGP framework to include the ability to infer joint estimates (full covariance matrices) for both translation and rotation, taking advantage of the fact that all estimates are correlated since they are derived from the same vehicle. We also modify the common zero mean assumption of a Gaussian process to accommodate a standard geometric model of the camera, thus providing an initial estimate that is then further refined by the non-parametric model. Both Gaussian process hyperparameters and camera parameters are trained simultaneously, so there is still no need for traditional camera calibration, although if these values are known they can be used to speed up training. This approach has been tested in a wide variety of situations, both 2D in urban and off-road environments (two degrees of freedom) and 3D with unmanned aerial vehicles (six degrees of freedom), with results that are comparable to standard state-of-the-art visual odometry algorithms and even more traditional methods, such as wheel encoders and laser-based Iterative Closest Point. We also test its limits to generalize over environment changes by varying training and testing conditions independently, and also by changing cameras between training and testing.