Thursday, July 31, 2008

Lab Meeting August 4th, 2008(Szu-Wei) Calbration of Ground Truth Labeling System

In this meeting, I will introduce the structure of Leo's "Ground Truth Labeling System." and show how to improve accuracy of camera calibration in this system.

Lab Meeting (2008/8/4) (Chung-Han):Random Sample Consensus: A Paradigm for Model Fitting with Apphcatlons to Image Analysis and Automated Cartography

Title : Random Sample Consensus: A Paradigm for Model Fitting with Apphcatlons to Image Analysis and Automated Cartography.

Authors : Martin A. Fischler and Robert C. Bolles SRI International(June, 1981)

Abstract :

A new paradigm, Random Sample Consensus
(RANSAC), for fitting a model to experimental data is
introduced. RANSAC is capable of interpreting/
smoothing data containing a significant percentage of
gross errors, and is thus ideally suited for applications
in automated image analysis where interpretation is
based on the data provided by error-prone feature
detectors. A major portion of this paper describes the
application of RANSAC to the Location Determination
Problem (LDP): Given an image depicting a set of
landmarks with known locations, determine that point
in space from which the image was obtained. In
response to a RANSAC requirement, new results are
derived on the minimum number of landmarks needed
to obtain a solution, and algorithms are presented for
computing these minimum-landmark solutions in closed
form. These results provide the basis for an automatic
system that can solve the LDP under difficult viewing
and analysis conditions. Implementation details and
computational examples are also presented.
Key Words and Phrases: model fitting, scene
analysis, camera calibration, image matching, location
determination, automated cartography.

Link : Full text

Lab Meeting July 31st, 2008 (Jimmy): Goal-Directed Pedestrian Model with Application to Robot Motion Planning

I will talk about the work done in my MS. thesis.

Lab Meeting July 31st, 2008 (Yu-Hsiang): Abnormal Activity Recognition by Learning and Inferring Scene Interaction

I will present my idea for abnormal activity recognition and some related works.

Wednesday, July 30, 2008

Lab Meeting July 31st, 2008 (swem): Method of determining hand waving signal

In the lab meeting, I will represent method of determining hand waving signal.

The representation will focus on
-Motion History Image
-Sobel gradient (Convolution Matrix filter)

Tuesday, July 22, 2008

Lab Meeting July 22nd, 2008 (Wei-Chun): Bearings-Only Tracking Problem

  • Bearings-only tracking.
  • Comparisons between regular and inverse-velocity representation form.
  • Modified gain extended Kalman filter
Please check your mailbox for the slides.

Monday, July 21, 2008

Lab Meeting July 22nd, 2008 (Jeff):Single Camera Vision-Only SLAM on a Suburban Road Network

Title: Single Camera Vision-Only SLAM on a Suburban Road Network

Authors: Michael J. Milford and Gordon F. Wyeth


Simultaneous Localization And Mapping (SLAM) is one of the major challenges in mobile robotics. Probabilistic techniques using high-end range finding devices are well established in the field, but recent work has investigated visiononly approaches. This paper presents a method for generating approximate rotational and translation velocity information from a single vehicle-mounted consumer camera, without the computationally expensive process of tracking landmarks. The method is tested by employing it to provide the odometric and
visual information for the RatSLAM system while mapping a complex suburban road network. RatSLAM generates a coherent map of the environment during an 18 km long trip through suburban traffic at speeds of up to 60 km/hr. This result demonstrates the potential of ground-based vision-only SLAM using low cost sensing and computational hardware.

ICRA 2008 Paper
Please see the ICRA disk:0596.pdf

Sunday, July 20, 2008

Lab Meeting July 22th, 2008 (fish60): Planning Long Dynamically-Feasible Maneuvers for Autonomous Vehicles

Maxim Likhachev and Dave Ferguson
Proceedings of the Robotics: Science and Systems Conference (RSS), 2008

In this paper, we present an algorithm for generating complex dynamically-feasible maneuvers for autonomous vehicles traveling at high speeds over large distances. Our approachis based on performing anytime incremental search on a multi-resolution, dynamically-feasible lattice state space. The resulting planner provides real-time performance and guarantees on and control of the sub-optimality of its solution.


Saturday, July 19, 2008

Robot PAL Master Thesis Oral July 29 2008


Kao-Wei Wan

Place: CSIE R524
Time: 9:00 AM

Thesis Committee:
Chieh-Chih Wang (Chair)
Li-Chen Fu
Han-Pang Huang
Jenhwa Guo
Chu-Song Chen (Academia Sinica)

Robot PAL Master Thesis Oral July 21 2008


Te-Cheng Liu

Place: CSIE R524
Time: 4:30 PM

Thesis Committee:
Chieh-ChihWang (Chair)
Ruey-Feng Chang
Yung-Yu Chuang
Tyng-Luh Liu (Academia Sinica)

Robot PAL Master Thesis Oral July 18 2008


Yueh-chi Yu

Place: CSIE R524
Time: 3:00 pm

Thesis Committee:
Chieh-Chih Wang (Chair)
Cheng-yuan Liou
Feng-Li Lian
Pei-Chun Lin
Tsai-Yen Li (National Chengchi University)

Wednesday, July 16, 2008

CVPR 2008 Best Papers

Best Paper
Beyond Sliding Windows: Object Localization by Efficient Subwindow Search, Christoph H. Lampert, Matthew B.Blaschko,Thomas Hofmann

Best Paper
Global Stereo Reconstruction under Second Order Smoothness Priors, Oliver Woodford, Ian Reid, Philip Torr, Andrew Fitzgibbon

Best Student Paper
Fast Image Search for Learned Metrics, Prateek Jain, Brian Kulis, Kristen Grauman

Best Poster
The Patch Transform and its Applications to Image Editing, Taeg Sang Cho, Moshe Butman, Shai Avidan, William Freeman

Best Student Poster
Robust dual motion deblurring, Jia Chen, Lu Yuan, Chi-Keung Tang, Long Quan

Sunday, July 13, 2008

Robotics Institute Thesis Oral 17 Jul 2008

Integrating Perception & Planning for Humanoid Autonomy

Philipp Michel
Robotics Institute
Carnegie Mellon University

This thesis explores appropriate approaches to perception on humanoids and ways of coupling sensing and planning to generate navigation and manipulation strategies that can be executed reliably.
We examine how predictive information about the future state of the world gathered from observation enables navigation in the presence of challenging moving obstacles. We show how programmable graphics hardware can be exploited to create a novel, model-based 3D tracking system able to robustly address the difficulties of real-time sensing specifically encountered on a locomoting humanoid. This thesis argues furthermore that reliability of autonomous operation can be improved by reasoning about perception during the planning process, rather than maintaining the traditional separation of the sensing and planning stages.

Monday, July 07, 2008

Lab Meeting July 7th, 2008 (Casey): A Fast Local Descriptor for Dense Matching

Title:A Fast Local Descriptor for Dense Matching
Authors:Engin Tola, Vincent Lepetit, Pascal Fua
We introduce a novel local image descriptor designed for dense wide-baseline matching purposes. We feed our descriptors to a graph-cuts based dense depth map estimation algorithm and this yields better wide-baseline performance than the commonly used correlation windows for which the size is hard to tune. As a result, unlike competing techniques that require many high-resolution images to produce good reconstructions, our descriptor can compute them from pairs of low-quality images such as the ones captured by video streams.Our descriptor is inspired from earlier ones such as SIFT and GLOH but can be computed much faster for our purposes. Unlike SURF which can also be computed efficientlyat every pixel, it does not introduce artifacts that degrade the matching performance.Our approach was tested with ground truth laser scanned depth maps as well as on a wide variety of image pairs of different resolutions and we show that good reconstructions are achieved even with only two low quality images.


Lab Meeting July 7th, 2008 (Atwood): Progress Report

I will talk about my recent experiments on hand posture.

Sunday, July 06, 2008

Lab Meeting July 7th, 2008 (Any): Classifying Dynamic Objects: An Unsupervised Learning Approach

Title: Classifying Dynamic Objects: An Unsupervised Learning Approach
Authors: Matthias Luber, Kai O. Arras, Christian Plagemann, and Wolfram Burgard
Abstract: For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an unsupervised learning approach to this model-building problem. We describe an exemplar-based model for representing the time-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given training sequences. Extensive experiments in real environments demonstrate that our system is able to autonomously learn useful models for, e.g., pedestrians, skaters, or cyclists without being provided with external class information.

PDF via Robotics: Science and Systems IV