Real-time large scale dense RGB-D SLAM with volumetric fusion

Thomas Whelan, Michael Kaess, Hordur Johannsson, Maurice Fallon, John J. Leonard and John McDonald
Journal Article, The International Journal of Robotics Research, Vol. 34, pp. 598-626, April, 2015

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We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.

author = {Thomas Whelan and Michael Kaess and Hordur Johannsson and Maurice Fallon and John J. Leonard and John McDonald},
title = {Real-time large scale dense RGB-D SLAM with volumetric fusion},
journal = {The International Journal of Robotics Research},
year = {2015},
month = {April},
volume = {34},
pages = {598-626},
} 2019-06-26T15:52:16-04:00