Incorporating Ego-motion Uncertainty Estimates in Range Data Registration

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Henrik Andreasson, Daniel Adolfsson, Todor Stoyanov, Martin Magnusson, and Achim J. Lilienthal
Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
to appear in Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS) 2017

Abstract

Local scan registration approaches commonly only utilize ego-motion estimates (e.g. odometry) as an initial pose guess in an iterative alignment procedure. This paper describes a new method to incorporate ego-motion estimates, including uncertainty, into the objective function of a registration algorithm. The proposed approach is particularly suited for feature-poor and self-similar environments, which typically present challenges to current state of the art registration algorithms. Experimental evaluation shows significant improvements in accuracy when using data acquired by Automatic Guided Vehicles (AGVs) in industrial production and warehouse environments.

@INPROCEEDINGS{andreasson2017egomotion, 
 author={Henrik Andreasson and Daniel Adolfsson and Todor Stoyanov and Martin Magnusson and  and Achim J. Lilienthal}, 
 booktitle={Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS)}, 
 title={Incorporating Ego-motion Uncertainty Estimates in Range Data Registration}, 
 year={2017}, 
 pages={}, 
 month=sep,
}