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On Degeneracy of Optimization-based State Estimation Problems

Ji Zhang, Michael Kaess and Sanjiv Singh
Conference Paper, Carnegie Mellon University, 2016 IEEE International Conference on Robotics and Automation, May, 2016

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Positioning and mapping can be conducted accurately by state-of-the-art state estimation methods. However, reliability of these methods is largely based on avoiding degeneracy that can arise from cases such as scarcity of texture features for vision sensors and lack of geometrical structures for range sensors. Since the problems are inevitably solved in uncontrived environments where sensors cannot function with their highest quality, it is important for the estimation methods to be robust to degeneracy. This paper proposes an online method to mitigate for degeneracy in optimization-based problems, through analysis of geometric structure of the problem constraints. The method determines and separates degenerate directions in the state space, and only partially solves the problem in well-conditioned directions. We demonstrate utility of this method with data from a camera and lidar sensor pack to estimate 6-DOF ego-motion. Experimental results show that the system is able to improve estimation in environmentally degenerate cases, resulting in enhanced robustness for online positioning and mapping.

BibTeX Reference
title = {On Degeneracy of Optimization-based State Estimation Problems},
author = {Ji Zhang and Michael Kaess and Sanjiv Singh},
booktitle = {2016 IEEE International Conference on Robotics and Automation},
school = {Robotics Institute , Carnegie Mellon University},
month = {May},
year = {2016},
address = {Pittsburgh, PA},