The Borg Lab

DDF-SAM 2.0: Consistent Distributed Smoothing and Mapping

TitleDDF-SAM 2.0: Consistent Distributed Smoothing and Mapping
Publication TypeConference Paper
Year of Publication2013
AuthorsCunningham A, Indelman V, Dellaert F
Conference Name2013 IEEE International Conference on Robotics and Automation (ICRA)
Date Published05/2013
Conference LocationKarlsruhe, Germany
KeywordsDecentralized SLAM, Mapping, Multi-robot

This paper presents an 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 approach to avoiding information double-counting in the previous DDF-SAM algorithm. 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. This paper presents and compares three summarization techniques, with two exact approaches and an approximation. We evaluated the proposed system in a synthetic example and show the augmented local system and the associated summarization technique do not double-count information, while keeping performance tractable.

Citation KeyCunningham13icra