Unsupervised Sensor Invariant Indoor Mapping and Localization

There are many methods for mapping and localization based on sensor measurements and a known functional model. In these methods the creation of the map is performed by applying the functional model on the sensor measurements. The problem is that many times the model is unknown or very complicated.
This project introduces a mapping algorithm based on measurements. The algorithm uses unsupervised learning implementing a manifold learning technique that does not require a model nor depends on measurements type, thus its advantage. The algorithm was implemented in Matlab and tested using measurements (panorama images) taken from a real room model simulation implemented in Blender.
The results show the algorithm succeed in reconstruction of locations map based on measurements, for convex as well as non convex maps.

Unsupervised Sensor Invariant Indoor Mapping and Localization

 

Unsupervised Sensor Invariant Indoor Mapping and Localization
Unsupervised Sensor Invariant Indoor Mapping and Localization