The two papers below will be presented at the 2011 ICRA conference in Shanghai, China.

The first one is about learning to predict which 3D features from a large environment model will be detected in a camera image, which can cut down tremendously on computational cost in localizing the camera, e.g., in robot or phone localization:

1. •Visibility Learning for Large-Scale Urban Environment, Pablo Fernández Alcantarilla, Kai Ni, Luis Miguel Bergasa, and Frank Dellaert, IEEE International Conference on Robotics and Automation (ICRA), 2011
   

The second one is a very exciting new incremental optimization technique, iSAM 2, which we apply to mapping and localization in robotics. We represent what we know about the robot’s environment as a large tree data structure called the Bayes tree, which we iteratively update as more measurements come in via the robot’s sensors:

1. •iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering, Michael Kaess, Hordur Johannsson, Richard Roberts, Viorela Ila, John Leonard, and Frank Dellaert, IEEE International Conference on Robotics and Automation (ICRA), 2011
   

You can see more pictures and a movie about iSAM 2 here.