  Low cost robots, such as vacuum cleaners or lawn mowers employ simplistic and
often random navigation policies. Although a large number of sophisticated
mapping and planning approaches exist, they require additional sensors like
LIDAR sensors, cameras or time of flight sensors. In this work, we propose a
loop closure detection method based only on odometry data which can be
generated using low-range or binary signal sensors together with simple wall
following techniques. We show how to include the detected loop closing
constraints into a pose graph formulation such that standard pose graph
optimization techniques can be used for map estimation. We evaluate our map
estimate and loop closure approach using both, simulation and a real lawn mower
in complex and realistic environments. Our results demonstrate that our
approach generates accurate map estimates on the basis of odometry data only.
We further show that our assumption about the discriminative nature of
neighboring poses in the pose graph is solid, even under large odometry noise.
These improved map estimates provide the basis for smart navigation policies in
low cost robots and extends their abilities to goal-directed behavior like pick
and place or complete coverage path planning in realistic environments.
