GTP-SLAM: Game-Theoretic Priors for Simultaneous Localization and Mapping in Multi-Agent Scenarios

TL;DR

GTP-SLAM introduces a game-theoretic approach to multi-agent SLAM, effectively modeling non-cooperative interactions and improving localization and mapping accuracy in complex environments.

Contribution

It presents a novel SLAM algorithm that incorporates game-theoretic priors and potential game formulation for better multi-agent environment modeling.

Findings

Outperforms standard bundle adjustment in accuracy.

Provides strong convergence guarantees.

Effective in realistic traffic simulations.

Abstract

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.