论文标题
GTP-SLAM：在多代理方案中同时本地化和映射的游戏理论先验
GTP-SLAM: Game-Theoretic Priors for Simultaneous Localization and Mapping in Multi-Agent Scenarios
论文作者
论文摘要
在多游戏设置中运行的机器人必须同时对共享环境的人或机器人代理的环境和行为进行建模。这种建模通常是使用同时定位和映射（SLAM）进行的。但是，SLAM算法通常会忽略多玩家的相互作用。相比之下，运动计划文献经常使用动态游戏理论来明确对具有完美本地化的已知环境中多个代理的非合作相互作用进行建模。在这里，我们介绍了GTP-Slam，这是一种基于迭代最佳响应的小说最佳响应算法，可以准确执行状态定位和映射重建，同时使用游戏理论先验来捕获未知场景中多个代理之间固有的非合作互动。通过将基本的大满贯问题作为潜在游戏，我们继承了强大的融合保证。经验结果表明，当部署在现实的交通模拟中时，我们的方法比在广泛的噪声水平上的标准捆绑捆绑调节算法更准确地执行本地化和映射。
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.
