AI Seminar

Toyota AI Seminar | Place Recognition from Perceptually Ambiguous Data

Edwin Olson

Assistant Professor
Computer Science and Engineering
Tuesday, September 23, 2008
4:00pm - 5:30pm
Stained-Glass Conference Room (3725 Beyster Bldg.)

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About the Event

The ability to recognize places is a critical component of robot mapping and navigation systems. Unfortunately, place recognition is complicated by two major problems. The first challenge is that the uncertainty in the robot's position (as provided by dead-reckoning sensors) grows very quickly. When recognizing places, this uncertainty means that the robot must consider a large number of possible answers. The second challenge is that current sensing systems often produce similar readings for distinct places, making it difficult to distinguish subtly different environments. This problem is further compounded by the need to robustly recognize places even if the appearance of those places changes. In this talk, we describe several approaches that can be used to reliably recognize places even when the prior uncertainty is large and when the sensor data is highly ambiguous. Our approach combines data collected over a large spatial area and uses a spectral graph method to identify a cluster of data that is maximally self-consistent. The resulting inlier clusters must also satisfy a probabilistically-motivated geometrical sufficiency test. The resulting method allows robots to recognize places more often and more robustly, leading to improved navigational performance. Other ongoing robotics-related research projects will be briefly described, time permitting.

Additional Information

Contact: Jonathan Sorg

Email: jdsorg@umich.edu

Sponsor(s): Toyota AI Seminar Series

Open to: Public