This paper presents an analysis of large scale decentralized SLAM under a variety of experimental conditions
to illustrate design trade-offs relevant to multi-robot mapping in challenging environments. As a part of work
through the MAST CTA, the focus of these robot teams is on the use of small-scale robots with limited sensing,
communication and computational resources. To evaluate mapping algorithms with large numbers (50+) of
robots, we developed a simulation incorporating sensing of unlabeled landmarks, line-of-sight blocking obstacles,
and communication modeling. Scenarios are randomly generated with variable models for sensing, communication,
and robot behavior.
The underlying Decentralized Data Fusion (DDF) algorithm in these experiments enables robots to construct a
map of their surroundings by fusing local sensor measurements with condensed map information from neighboring
robots. Each robot maintains a cache of previously collected condensed maps from neighboring robots, and
actively distributes these maps throughout the network to ensure resilience to communication and node failures.
We bound the size of the robot neighborhoods to control the growth of the size of neighborhood maps.
We present the results of experiments conducted in these simulated scenarios under varying measurement
models and conditions while measuring mapping performance. We discuss the trade-offs between mapping
performance and scenario design, including robot teams separating and joining, multi-robot data association,
exploration bounding, and neighborhood sizes.