DDF-SAM 2. 0: Consistent distributed smoothing and mapping

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.

Authors

• Alexander Cunningham
• Vadim Indelman
• Frank Dellaert

Keywords

• Simultaneous localization and mapping
• Approximation methods
• Zirconium
• Silicon
• Local Information
• Local System
• Local Map
• Graphics System
• Neighborhood Information
• Unknown Environment
• Augmented System
• Exact Approach
• Linear System
• Local Measurements
• Local Solution
• Global Map
• Subset Of Variables
• Posterior Mode
• Map Information
• Robotic Platform
• Extended Kalman Filter
• Upper Triangular
• Local Updates
• Bayberry
• Factor Graph
• Bayesian Model
• Single Robot
• Joint Density
• Partial Elimination
• Elimination Algorithm
• Elimination Of Variables
• Swarm Robotics
• Linear Solver