DDF-SAM: Fully distributed SLAM using Constrained Factor Graphs

We address the problem of multi-robot distributed SLAM with an extended Smoothing and Mapping (SAM) approach to implement Decentralized Data Fusion (DDF). We present DDF-SAM, a novel method for efficiently and robustly distributing map information across a team of robots, to achieve scalability in computational cost and in communication bandwidth and robustness to node failure and to changes in network topology. DDF-SAM consists of three modules: (1) a local optimization module to execute single-robot SAM and condense the local graph; (2) a communication module to collect and propagate condensed local graphs to other robots, and (3) a neighborhood graph optimizer module to combine local graphs into maps describing the neighborhood of a robot. We demonstrate scalability and robustness through a simulated example, in which inference is consistently faster than a comparable naive approach.

Authors

• Alexander Cunningham
• Balamanohar Paluri
• Frank Dellaert

Keywords

• Optimization
• Robot kinematics
• Simultaneous localization and mapping
• Mathematical model
• Equations
• Factor Graph
• Condensation
• Scalable
• Mapping Approach
• Network Topology
• Data Fusion
• Topological Changes
• Optimal Mode
• Local Module
• Naive Approach
• Communication Bandwidth
• Communication Module
• Swarm Robotics
• Node Failure
• Changes In Network Topology
• Local Graph
• Smoothing Approach
• Probabilistic Model
• Reference Frame
• Set Of Robots
• Least Squares Problem
• Nonlinear Programming
• Back-end System
• Local Map
• Nonlinear Least Squares Problem
• Multiple Robots
• Hard Constraints
• Constrained Optimization
• Linear Problem