Monday, December 17, 2012

Lab Meeting December 19th, 2012 (Jeff): Inference on networks of mixtures for robust robot mapping

Title: Inference on networks of mixtures for robust robot mapping

Authors: Edwin Olson and Pratik Agarwal


The central challenge in robotic mapping is obtaining reliable data associations (or “loop closures”): state-of-the-art inference algorithms can fail catastrophically if even
one erroneous loop closure is incorporated into the map. Consequently, much work has been done to push error rates closer to zero. However, a long-lived or multi-robot system will
still encounter errors, leading to system failure.

We propose a fundamentally different approach: allow richer error models that allow the probability of a failure to be explicitly modeled. In other words, we optimize the map while
simultaneously determining which loop closures are correct from within a single integrated Bayesian framework. Unlike earlier multiple-hypothesis approaches, our approach avoids
exponential memory complexity and is fast enough for realtime performance.

We show that the proposed method not only allows loop closing errors to be automatically identified, but also that in extreme cases, the “front-end” loop-validation systems can be unnecessary. We demonstrate our system both on standard benchmarks and on the real-world datasets that motivated this work.

Robotics: Science and Systems(RSS), 2012

Monday, December 10, 2012

Lab meeting Dec. 12, 2012 (Alan): A Simple Prior-free Method for Non-Rigid Structure-from-Motion Factorization (CVPR 2012 Best Paper Award)

Title: A Simple Prior-free Method for Non-Rigid Structure-from-Motion Factorization (CVPR 2012 Best Paper Award)
Authors: Yuchao Dai, Hongdong Li, Mingyi He

This paper proposes a simple “prior-free” method for solving non-rigid structure-from-motion factorization problems. Other than using the basic low-rank condition, our method does not assume any extra prior knowledge about the nonrigid scene or about the camera motions. Yet, it runs reliably, produces optimal result, and does not suffer from the inherent basis-ambiguity issue which plagued many conventional nonrigid factorization techniques.

Our method is easy to implement, which involves solving no more than an SDP (semi-definite programming) of small and fixed size, a linear Least-Squares or trace-norm minimization. Extensive experiments have demonstrated that it outperforms most of the existing linear methods of nonrigid factorization. This paper offers not only new theoretical insight, but also a practical, everyday solution, to non-rigid structure-from-motion.


Tuesday, December 04, 2012

Lab meeting Dec 5th 2012 (Jim): Imitation Learning by Coaching

Title: Imitation Learning by Coaching
Authors: He He, Hal Daumé III and Jason Eisner
Neural Information Processing Systems (NIPS), 2012

... we propose to use a coach that demonstrates easy-to-learn actions for the learner and gradually approaches the oracle. ... We apply our algorithm to cost-sensitive dynamic feature selection, a hard decision problem that considers a user-specified accuracy-cost trade-off. ...