Consistent Decentralized Graphical SLAM with Anti-Factor Down-Dating
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This report presents our recent and ongoing work developing a consistent decentralized data fusion approach for robust multi-robot SLAM in dangerous, unknown environments. The DDF-SAM 2.0 approach extends our previous work by combining local and neighborhood information in a single, consistent augmented local map, without the overly conservative to avoiding information double-counting in the previous DDF-SAM approach. We introduce the anti-factor as a means to subtract information in graphical SLAM systems, and illustrate its use to both replace information in an incremental solver and to cancel out neighborhood information from shared summarized maps. Evaluations in a synthetic example environment demonstrate that we avoid double-counting information.