Vision-based Aerial Mapping
With this project we hope to push the limits of what can be done with monocular vision, a rich and ubiquitous sensor for mobile robots. This work combines principles of robotics, particularly probabilistic robotics, with well-established results in computer vision, especially the subfield known as "structure from motion."
Mapping with a single camera is a compelling problem because we humans have no trouble wandering a new environment (even with one eye closed). What's more, we can subsequently use that experience to get around and perform tasks.
We will be using the ARDrone 2 quadcopter as the platform for our vision-based control and mapping efforts in 2012. In addition, we will be using ground platforms including the Neato (with its built-in laser ranger) and the iRobot Create with a Kinect. The aerial platforms are an example where a Kinect or laser cannot completely replace vision: they are too heavy and power-hungry for the drone to support.
We certainly cannot hope to re-derive the amanzing progress in this field! Thus, the 2013 REU project will look into using the remarkable monocular slam work of Andrew Davison and his students as a starting point and/or inspiration A concrete goal is to use autonomous navigation of our indoor space to build a 3d model that, in turn, could support virtual fly-throughs of that environment.
Previous Work
In 2012, Vivian Wehner '14, Antonella Wilby (UCSD '14), David Greene (Puget Sound '13), and Lena Reed '14 built top-to-botom systems for human-scale navigation within Mudd's mazelike Libra Complex; they also implemented and deployed a system for multirobot coordination. The team presented their work at the 2012 AAAI AI and Robotics Multimedia Fair in Toronto and saw Andrew Ng, Sebastian Thrun, and many other AI and robotics speakers at AAAI '12. The team's work also furthered a novel CS curriculum based on project-based robotics, as presented at EAAI 2012, deployed at HMC and Glendale Community College, and will be the basis of a workshop at SIGCSE 2013.
In 2011, Brad Jensen '13, Lilian de Greef '12, Kim Sheely '12, Malen Sok '13, and Nick Berezny '12 developed software for our aerial platforms spanning from device drivers to vision-based localization routines using SURF features. They exhibited their work at GCER 2011 and AAAI 2011's robot exhibition and workshop. The team also attended RSS 2011 in Los Angeles and their work was presented at TePRA 2012.
In 2010, Nicole Lesperance and Michael Leece led a team of students in creating a top-to-bottom vision system that estimated a robot's local environment from its webcamera. Their vision system was published and presented in The 2010 International Symposium on Visual Computing, and a broader view of their perception work and its control of an iRobot Create was published and presented at the 2011's IEEE TePRA conference. Perhaps the highlight of the summer was the team's exhibit at the 2010 AAAI Robotics workshop and exhibition in Atlanta. In addition to exhibiting their autonomously navigating Create, the team took advantage of the venue to watch some of the first annual robot chess competition and several humanoid-robot obstacle course runs.
Mentor: Professor Zach Dodds
Zach has been a professor
at Harvey Mudd since 1999. His general research interests are in
computer vision and robotics. He received his B.A. in mathematics
and M.S. and Ph.D. in computer science from Yale University. In
addition to research and teaching, he likes to play in foam pits
with his children.
Required Background
While background in computer vision and robotics/robotics algorithms is wonderful, it's not required. Students interested in working on this project should have completed at least a course in C++ programming and data structures and should feel confident in applying their programming ability through new frameworks and for new problems (e.g., vision & robotics...). We will use Python a lot, too, but it is so easy to learn that it's not worth worrying about beforehand.
