Long-term Large-scale Mapping and Localization Using maplab
This work addresses mapping and localization challenges for robotics and autonomous systems, but it appears incremental as it builds on the existing maplab framework.
The paper tackles the problem of long-term, large-scale mapping and localization by using the maplab open-source framework to build consistent maps from multiple sessions, demonstrating that these maps can be reused months later for efficient 6-DoF localization and registering new trajectories within existing 3D models.
This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework. We present a brief overview of the specific algorithms in the system that enable building a consistent map from multiple sessions. We then demonstrate that such a map can be reused even a few months later for efficient 6-DoF localization and also new trajectories can be registered within the existing 3D model. The datasets presented in this paper are made publicly available.