[talks] Colloquium Speaker Nikolay Atanasov, Friday Feb 26, 3:30pm

Nicole E. Wagenblast nwagenbl at CS.Princeton.EDU
Tue Feb 23 12:04:11 EST 2016

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. 
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