SLAM in Weak Environment Information Applications using Swarm Robots

Abstract

Abstract Simultaneous Localisation and Mapping (SLAM) in weak environment information scenarios where there are only sparse and ambiguous references available presents significant challenges. Feature sparsity and repetitive elements of landmarks impose difficulties in establishing stable tracking and lead perception overlap. The point-line matching based approach presented in this paper provides additional geometric context that makes it easier to distinguish between similar features. This method identifies landmarks as points, extrapolates lines from consecutive point observations, and generates point-line pairs using geometric and appearance-based criteria. The pairs serve as unique identifiers for locations and, hence, effectively solves the identical perceptions problem. To optimize this solution further, we introduce the generation of trajectory graphs, allowing the swarm to refine its map and location estimates iteratively. Experimental results validate that our approach outperforms traditional methods in the challenging weak environment information applications.