Colloquium Speaker

Dr. Nikolay Atanasov
Acquiring Metric and Semantic Information using Autonomous Robots
Friday, February 26th at 3:30 PM
Maeder Hall, ACEE

Recent years have seen impressive progress in robot control and
perception including adept manipulation, aggressive quadrotor maneuvers,
dense metric map reconstruction, and object recognition in real time.
The grand challenge in robotics today is to capitalize on these advances
in order to enable autonomy at a higher-level of intelligence.  It is
compelling to envision teams of autonomous robots in environmental
monitoring, precision agriculture, construction and structure
inspection, security and surveillance, and search and rescue.

In this talk, I will emphasize that many such applications can be
addressed by thinking about how to coordinate robots in order to extract
useful information about the environment. More precisely, I will
formulate a general active estimation problem that captures the common
characteristics of the aforementioned scenarios. I will show how to
manage the complexity of the problem over metric information spaces with
respect to long planning horizons and large robot teams. These results
lead to computationally scalable, non-myopic algorithms with quantified
performance for problems such as distributed source seeking and active
simultaneous localization and mapping (SLAM).

I will then focus on acquiring information using both metric and
semantic observations (e.g., object recognition).  In this context,
there are several new challenges such as missed detections, false
alarms, and unknown data association.  To address them, I will model
semantic observations via random sets and will discuss filtering using
such models. A major contribution of our approach is in proving that the
complexity of the problem is equivalent to computing the permanent of a
suitable matrix. This enables us to develop and experimentally validate
algorithms for semantic localization, mapping, and planning on mobile
robots, Google's project Tango phone, and the KITTI visual odometry dataset.