Collaborative Localization of Aerial and Ground Mobile Robots through Orthomosaic Map
This addresses the challenge of precise localization in multi-robot systems with varied sensors, but it is incremental as it builds on existing SLAM and matching techniques.
The paper tackles the problem of cooperative SLAM for aerial and ground robots by proposing a map matching and localization approach to align maps with large viewpoint scale differences, enabling ground robot localization in a drone's global map. It demonstrates accuracy, robustness, and speed on the Aero-Ground Dataset.
With the deepening of research on the SLAM system, the possibility of cooperative SLAM with multi-robots has been proposed. This paper presents a map matching and localization approach considering the cooperative SLAM of an aerial-ground system. The proposed approach aims to help precisely matching the map constructed by two independent systems that have large scale variance of viewpoints of the same route and eventually enables the ground mobile robot to localize itself in the global map given by the drone. It contains dense mapping with Elevation Map and software "Metashape", map matching with a proposed template matching algorithm, weighted normalized cross-correlation (WNCC) and localization with particle filter. The approach enables map matching for cooperative SLAM with the feasibility of multiple scene sensors, varies from stereo cameras to lidars, and is insensitive to the synchronization of the two systems. We demonstrate the accuracy, robustness, and the speed of the approach under experiments of the Aero-Ground Dataset.