Lab meeting Mar. 20, (Andi) Multiview Structure from Motion in Trajectory Space

Authors: Aamer Zaheer, Ijaz Akhter, Mohammad Haris Baig, Shabbir Marzban, Sohaib Khan

Abstract:
Most nonrigid objects exhibit temporal regularities in their deformations. Recently it was proposed that these regularities can be parameterized by assuming that the non-rigid structure lies in a small dimensional trajectory space. In this paper, we propose a factorization approach for 3D reconstruction from multiple static cameras under the compact trajectory subspace representation. Proposed factorization is analogous to rank-3 factorization of rigid structure from motion problem, in transformed space. The benefit of our approach is that the 3D trajectory basis can be directly learned from the image observations. This also allows us to impute missing observations and denoise tracking errors without explicit estimation of the 3D structure. In contrast to standard triangulation based methods which require points to be visible in at least two cameras, our approach can reconstruct points, which remain occluded even in all the cameras for quite a long time. This makes our solution especially suitable for occlusion handling in motion capture systems. We demonstrate robustness of our method on challenging real and synthetic scenarios.

 In: Proceedings of the 13th International Conference on Computer Vision (ICCV), Barcelona, Spain, Nov 2011

download paper

Lab meeting Mar. 13, (ChihChung) Matching two scene images with large distance and view angle change.

In this reporting, I will present the recent state-of-art approaches for scene image matching tasks and then discuss several new ideas of mine.

The references are:

Algorithms:

Affine-invariant SIFT:
link1
link2
link3

ORSA(Optimized RANSAC):
link

Virtual-line descriptor:
link

1-point RANSAC:
link

Implementation:

Using MAV and google street map for visual localization:
link

Lab Meeting March 6th, 2014 (Jeff): Simultaneous Parameter Calibration, Localization, and Mapping

Title: Simultaneous Parameter Calibration, Localization, and Mapping

Authors: Rainer Kümmerle, Giorgio Grisetti, and Wolfram Burgard

Abstract:

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms.


Advanced Robotics Vol.26, 2012

Link:

http://www.tandfonline.com/doi/full/10.1080/01691864.2012.728694

Reference Link:
Simultaneous Parameter Calibration, Localization, and Mapping for Robust Service Robotics.
ARSO2011.
http://europa.informatik.uni-freiburg.de/files/kuemmerle11arso.pdf
Simultaneous Calibration, Localization, and Mapping.
IROS2011.
http://ais.informatik.uni-freiburg.de/publications/papers/kuemmerle11iros.pdf?origin=publication_detail