Martin Jagersand, PhD

Professor, Faculty of Science - Computing Science

Contact

Professor, Faculty of Science - Computing Science
Email
mj7@ualberta.ca

Overview

About

Martin Jagersand home page

Education

  • BSc, M.Sc., Engineering Physics, Chalmers University (Sweden)
  • M.Sc., Computer Science, University of Rochester
  • Engineering Licenciate, Chalmers 
  • Ph.D., Computer Science, University of Rochester
  • Post Doc, Computer Science, Yale University

Work experience

  • Research faculty, Computer-Integrated Surgical Systems and Technology, Johns Hopkins University
  • Researcher, Robotics and Mechatronics Institute, German Aerospace Institute (DLR), Wessling Germany.
  • Professor, University of Alberta

Research

Robot Vision research group home page

Areas

  • Robotics
  • Computer Vision 
  • Graphics

Summary

My group researches and develops real-time 2D and 3D video tracking, 3D modeling of geometry and appearance from camera vision, on-line camera-scene-robot kinematic model estimation, and vision guided motion control for robot arms and hands. Our aim is to make these methods robust so they work in unstructured natural environments such as outdoors or in human homes. Applications that we work on include first-response robotics in dangerous or remote areas, to assess damage, and turn off damaged infrastructure (shut fluid valves, turn off electricity breakers etc.); tele-robotic systems for on-orbit Satellite repair; assistance robotics for the elderly and disabled; better human-robot collaboration, so humans can work with robots, not separately from robot work cells. We have developed 3D modeling from video and images for both objects and scenes, and we have expertise in how to extract precise geometric measurements from images and video. 

Vision

A human can easily pick up a visible object, and can even watch a whole task, learn, and transform the visual information into the necessary motor (muscle) movements to carry out the task. In the other direction a human can visually imagine the execution of a motor task. As a contrast, conventional robotics does not solve these tasks easily or reliably, nor can a human easily instruct the robot what to do. One pitfall is that typically all necessary information needs to be programmed a-priori into a single global metrically calibrated model.

The centerpiece of my research is to instead estimate from observations only several local models and transforms on-line as they are needed. During a complete task, the attention shifts between different objects and frames, and new models need to be quickly acquired. Visual structure and appearance change is parameterized in a motor frame instead of a purely visual basis as in traditional viewing models. An advantage is that it automatically captures the actual freedoms of the object, e.g. an arm is parameterized in its joint coordinates, not as separate links.

Courses

CMPUT 312 - Introduction to Robotics and Mechatronics

Algorithms and software paradigms for robot programming; mathematical modeling of robot arms and rovers including kinematics, and an introduction to dynamics and control; sensors, motors and their modeling; basics of image processing and machine vision; vision-guided motion control. Prerequisite: CMPUT 275. Corequisite: CMPUT 340 or 418, or ECE 240. Students having CMPUT 174, 175, 201, 204 may seek permission from the Department.


CMPUT 428 - Computer Vision

Introduction to the geometry and photometry of the 3D to 2D image formation process for the purpose of computing scene properties from camera images. Computing and analyzing motion in image sequences. Recognition of objects (what) and spatial relationships (where) from images and tracking of these in video sequences. Prerequisites: CMPUT 201 or 275; one of CMPUT 340, 418, ECE 240, or equivalent knowledge; one of MATH 101, 115, 118, 136, 146 or 156, and one of MATH 102, 125, or 127.


CMPUT 615 - Topics in Image Processing and Vision


Browse more courses taught by Martin Jagersand