GraphBot Technical Program
8:30 - 8:40 | Welcome Remarks | Rudolph Triebel |
Session 1: Graphical Models in SLAM I | ||
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8:40 - 9:10 | The Bayes Tree and Inference in Large-Scale Graphical Models for SLAM and SFM | Frank Dellaert |
9:10 - 9:40 | iSAM and the Bayes Tree | Michael Kaess |
9:40 - 10:00 | Hybrid Hessians for Real-Time, Collaborative SLAM of Large Outdoor Environments | Matthew Koichi Grimes and Yann LeCun |
Session 2: Training and Inference in Graphical Models | ||
10:20 - 10:50 | Lifted Message Passing | Kristian Kersting |
10:50 - 11:20 | Linear Programming Decompositions for Distributed, Anytime Inference in Graphical Models | Fabio Ramos |
11:20 - 11:40 | An EM Algorithm for Affordance Learning with Probabilistic Clustering | Pedro Osório, Alexandre Bernadino, Ruben Martinez-Cantin, and José Santos-Victor |
11:40 - 14:00 | Lunch Break | |
Session 3: Graphical Models in SLAM II | ||
14:00 - 14:30 | Graphical Models for visual SLAM | Kurt Konolige |
14:30 - 14:50 |
A Balanced Distributed Graph-based Framework for
Multi-Robot Mapping
Video multimap.avi Video pal1.avi |
Dario Lodi Rizzini and Stefano Caselli |
Session 4: Graphical Models for Robot Perception | ||
15:30 - 16:00 | Graphical Models for Machine Perception | Edwin Olson |
16:00 - 16:30 | A Probabilistic Framework for Learning Kinematic Models of Articulated Objects | Wolfram Burgard |
16:30 - 16:50 | Probabilistic Temporal Prediction for Proactive Action Selection | Woo Young Kwon and Il Hong Suh |
16:50 - 17:20 | Panel Discussion | Moderated by V. Ila, R. Triebel, and T. Vidal-Calleja |
Legend: | Invited Talk | Peer-reviewed Paper |