  Mapping is one of the crucial tasks enabling autonomous navigation of a
mobile robot. Conventional mapping methods output dense geometric map
representation, e.g. an occupancy grid, which is not trivial to keep consistent
for the prolonged runs covering large environments. Meanwhile, capturing the
topological structure of the workspace enables fast path planning, is less
prone to odometry error accumulation and does not consume much memory.
Following this idea, this paper introduces PRISM-TopoMap -- a topological
mapping method that maintains a graph of locally aligned locations not relying
on global metric coordinates. The proposed method involves learnable multimodal
place recognition paired with the scan matching pipeline for localization and
loop closure in the graph of locations. The latter is updated online and the
robot is localized in a proper node at each time step. We conduct a broad
experimental evaluation of the suggested approach in a range of photo-realistic
environments and on a real robot (wheeled differential driven Husky robot), and
compare it to state of the art. The results of the empirical evaluation confirm
that PRISM-Topomap consistently outperforms competitors across several measures
of mapping and navigation efficiency and performs well on a real robot. The
code of PRISM-Topomap is open-sourced and available at
https://github.com/kirillMouraviev/prism-topomap.
