Deformation-based loop closure for large scale dense RGB-D SLAM
Author(s)Whelan, Thomas; Kaess, Michael; McDonald, John; Leonard, John Joseph
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In this paper we present a system for capturing large scale dense maps in an online setting with a low cost RGB-D sensor. Central to this work is the use of an “as-rigid-as-possible” space deformation for efficient dense map correction in a pose graph optimisation framework. By combining pose graph optimisation with non-rigid deformation of a dense map we are able to obtain highly accurate dense maps over large scale trajectories that are both locally and globally consistent. With low latency in mind we derive an incremental method for deformation graph construction, allowing multi-million point maps to be captured over hundreds of metres in real-time. We provide benchmark results on a well established RGB-D SLAM dataset demonstrating the accuracy of the system and also provide a number of our own datasets which cover a wide range of environments, both indoors, outdoors and across multiple floors.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Mechanical Engineering
Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems
Institute of Electrical and Electronics Engineers (IEEE)
Whelan, Thomas, Michael Kaess, John J. Leonard, and John McDonald. “Deformation-Based Loop Closure for Large Scale Dense RGB-D SLAM.” 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (November 2013).
Author's final manuscript