Furthermore, 3D feature-based registration methods have never quite reached the robustness of 2D methods in visual SLAM.
Robust and accurate pose estimation is crucial for many applications in mobile robotics.
This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework.
On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure.
Then, we create a set of convex free-space clusters, which are the vertices of the topological map.
We show that we can build TSDFs faster than Octomaps, and that it is more accurate to build ESDFs out of TSDFs than occupancy maps.