Robust Map Fusion with Visual Attention Utilizing Multi-agent Rendezvous

Abstract

The map fusion for multi-robot simultaneous localization and mapping (SLAM) consistently combines robot maps built independently into the global map. An established approach to map fusion is utilizing rendezvous, which refers to an encounter between multiple agents, to calculate the transformation into the global map. However, previous works using rendezvous have a limitation in that they are unreliable for certain circumstances, where the amount of agent observations or overlapping landmarks is limited. This work proposes a novel map fusion system which robustly fuses local maps in challenging rendezvous that lack shared information. Our system utilizes the single visual perception from rendezvous and estimates the relative pose between agents with the DOPE. Then our scheme transforms local maps with an estimated relative pose and predicts the misalignment from approximated maps by utilizing the attention mechanism of the vision transformer. Comparisons with the Hough transform-based method show that ours is significantly better when the overlap between local maps is insufficient. We also verify the robustness of our system against a similar real-world scenario.