Monday, January 18, 2010
Authors: Rob Fergus, Yair Weiss, and Antonio Torralba
In : NIPS2009
With the advent of the Internet it is now possible to collect hundreds of millions of images. These images come with varying degrees of label information. “Clean labels” can be manually obtained on a small fraction, “noisy labels” may be extracted automatically from surrounding text, while for most images there are no labels at all. Semi-supervised learning is a principled framework for combining these different label sources. However, it scales polynomially with the number of images, making it impractical for use on gigantic collections with hundreds of millions of images and thousands of classes. In this paper we show how to utilize recent results in machine learning to obtain highly efficient approximations for semi-supervised learning that are linear in the number of images. Specifically, we use the convergence of the eigenvectors of the normalized graph Laplacian to eigenfunctions of weighted Laplace-Beltrami operators. Our algorithm enables us to apply semi-supervised learning to a database of 80 million images gathered from the Internet.
Sunday, January 17, 2010
Lab Meeting January 19, 2010(Chung-Han):A collection of outdoor robotic datasets with centimeter-accuracy ground truth
Author : Jose-Luis Blanco, Francisco-Angel Moreno, Javier-Gonzalez
In : Autonomous Robots (2009)
The lack of publicly accessible datasets with a reliable ground truth has prevented in the past a fair and coherent comparison of different methods proposed in the mobile robot Simultaneous Localization and Mapping (SLAM) literature. Providing such a ground truth becomes specially challenging in the case of visual SLAM, where the world model is 3-dimensional and the robot path is 6-dimensional. This work addresses both the practical and theoretical issues found while building a collection of six outdoor datasets. It is discussed how to estimate the 6-d vehicle path from readings of a set of three Real Time Kinematics (RTK) GPS receivers, as well as the associated uncertainty bounds that can be employed to evaluate the performance of SLAM methods. The vehicle was also equipped with several laser scanners, from which reference point clouds are built as a testbed for other algorithms such as segmentation or surface fitting. All the datasets, calibration information and associated software tools are available for download http://babel.isa.uma.es/mrpt/papers/dataset2009/.
Saturday, January 16, 2010
Mapping Large Urban Environments with GPS-Aided SLAM
December 03, 2009, 9:00 a.m., GHC 6115
Simultaneous Localization and Mapping (SLAM) has been an active area of research for several decades, and has become a foundation of indoor mobile robotics. However, although the scale and quality of results have improved markedly in that time period, no current technique can effectively handle city-sized urban areas.
The Global Positioning System (GPS) is an extraordinarily useful source of localization information. Unfortunately, the noise characteristics of the system are complex, arising from a large number of sources, some of which have large autocorrelation. Incorporation of GPS signals into SLAM algorithms requires using low-level system information and explicit models of the underlying system to make appropriate use of the information. The potential benefits of combining GPS and SLAM include increased robustness, increased scalability, and improved accuracy of localization.
This dissertation presents a theoretical background for GPS-SLAM fusion. The presented model balances ease of implementation with correct handling of the highly colored sources of noise in a GPS system.. This utility of the theory is explored and validated in the framework of a simulated Extended Kalman Filter driven by real-world noise.
The model is then extended to Smoothing and Mapping (SAM), which overcomes the linearization and algorithmic complexity limitations of the EKF formulation. This GPS-SAM model is used to generate a probabilistic landmark-based urban map covering an area an order of magnitude larger than previous work.
Charles Thorpe, Chair
Frank Dellaert, Georgia Institute of Technology
Sunday, January 03, 2010
Lab Meeting January 6th, 2010 (Any): Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies
Friday, January 01, 2010
Authors: Gabe Sibley, Christopher Mei, Ian Reid, Paul Newman
It is well known that bundle adjustment is the
optimal non-linear least-squares formulation of the simultaneous
localization and mapping problem, in that its maximum
likelihood form matches the definition of the Cramer Rao
Lower Bound. Unfortunately, computing the ML solution is
often prohibitively expensive – this is especially true during loop
closures, which often necessitate adjusting all parameters in a
loop. In this paper we note that it is precisely the choice of a single
privileged coordinate frame that makes bundle adjustment costly,
and that this expense can be avoided by adopting a completely
relative approach. We derive a new relative bundle adjustment,
which instead of optimizing in a single Euclidean space, works
in a metric-space defined by a connected Riemannian manifold.
Using an adaptive optimization strategy, we show experimentally
that it is possible to solve for the full ML solution incrementally
in constant time – even at loop closure. Our system also operates
online in real-time using stereo data, with fast appearance-based
loop closure detection. We show results for sequences of 23k
frames over 1.08km that indicate the accuracy of the approach.