Efficient constellation-based map-merging for semantic SLAM
Name
1809.09646.pdf
Description
Submitted version
Size
2.34 MB
Format
Unknown
Checksum (MD5)
098dab94a2d8714094600f224540fc0c
Author(s)
Frey, Kristoffer M.
Steiner, Ted J
How, Jonathan P
Date Issued
May 2019
Publisher
IEEE
Citation
2019. "Efficient constellation-based map-merging for semantic SLAM."
Version
Original manuscript
Abstract
© 2019 IEEE. Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments. When ambiguous measurements arise, conservatism often mandates that the measurement is discarded or a new landmark is initialized rather than risking an incorrect association. To address the inevitable 'duplicate' landmarks that arise, we present an efficient map-merging framework to detect duplicate constellations of landmarks, providing a high-confidence loopclosure mechanism well-suited for object-level SLAM. This approach uses an incrementally-computable approximation of landmark uncertainty that only depends on local information in the SLAM graph, avoiding expensive recovery of the full system covariance matrix. This enables a search based on geometric consistency (GC) (rather than full joint compatibility (JC)) that inexpensively reduces the search space to a handful of 'best' hypotheses. Furthermore, we reformulate the commonly-used interpretation tree to allow for more efficient integration of clique-based pairwise compatibility, accelerating the branch-and-bound max-cardinality search. Our method is demonstrated to match the performance of full JC methods at significantly-reduced computational cost, facilitating robust object-based loop-closure over large SLAM problems.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Charles Stark Draper Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8794452
