Efficient Constellation-Based Map-Merging for Semantic SLAM

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 loop-closure 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.

Authors

• Kristoffer M. Frey
• Ted J. Steiner
• Jonathan P. How

Keywords

• Simultaneous localization and mapping
• Semantics
• Covariance matrices
• Uncertainty
• Detectors
• Search problems
• Current measurement
• Semantic SLAM
• Covariance Matrix
• Local Information
• Object Detection
• Class Labels
• Global Information
• Block Diagonal
• Tree Search
• Position Uncertainty
• Local Frame
• Optimal Alignment
• Fraction Of The Cost
• Geometric Errors
• Candidate Pairs
• Factor Graph
• Drift Error
• Tight Upper Bound
• Binary Constraints
• Candidate Matches