Robots operating in multi-player settings must simultaneously model the environment and the behavior of human or robotic agents who share that environment. This modeling is often approached using Simultaneous Localization and Mapping (SLAM); however, SLAM algorithms usually neglect multi-player interactions. In contrast, the motion planning literature often uses dynamic game theory to explicitly model noncooperative interactions of multiple agents in a known environment with perfect localization. Here, we present GTP-SLAM, a novel, iterative best response-based SLAM algorithm that accurately performs state localization and map reconstruction, while using game theoretic priors to capture the inherent non-cooperative interactions among multiple agents in an uncharted 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.

翻译：在多玩家环境中操作的机器人必须同时模拟环境以及共享环境的人类或机器人代理人的行为。 这种模型往往使用同步本地化和绘图(SLAM)来进行; 然而, SLAM 算法通常忽视多玩家互动。 相反, 运动规划文献经常使用动态游戏理论来明确模拟多个代理人在已知环境中的不合作互动,并实现完全本地化。 在这里, 我们展示了GTP- SLAM, 这是一种新颖的、 迭接的最佳反应- 以SLAM 算法, 精确地进行状态本地化和地图重建, 同时使用游戏理论前缀来捕捉在未知场景中多个代理人之间固有的非合作性互动。 通过将潜在的 SLAM 问题作为潜在的游戏, 我们继承了一种强大的趋同保证。 想象结果显示, 在现实的交通模拟中, 我们的方法比标准的组合算法在广泛的噪音水平上进行更精确的本地化和绘图。