Robots operating in complex, multi-player settings must simultaneously model
the environment and the behavior of human or robotic agents who share that
environment. Environmental modeling is often approached using Simultaneous
Localization and Mapping (SLAM) techniques; however, SLAM algorithms usually
neglect multi-player interactions. In contrast, a recent branch of the motion
planning literature uses dynamic game theory to explicitly model noncooperative
interactions of multiple agents in a known environment with perfect
localization. In this work, we fuse ideas from these disparate communities to
solve SLAM problems with game theoretic priors. We present GTP-SLAM, a novel,
iterative best response-based SLAM algorithm that accurately performs state
localization and map reconstruction in an uncharted scene, while capturing the
inherent game-theoretic interactions among multiple agents in that scene. By
formulating the underlying SLAM problem as a potential game, we inherit a
strong convergence guarantee. Empirical results indicate that, when deployed in
a realistic traffic simulation, our approach performs localization and mapping
more accurately than a standard bundle adjustment algorithm across a wide range
of noise levels.Comment: 6 pages, 3 figure
