Automatic detection of pole-like structures in 3D urban environments

This work aims at automatic detection of man-made pole-like structures in scans of urban environments acquired by a 3D sensor mounted on top a moving vehicle. Pole-like structures, such as e.g. roadsigns and streetlights, are widespread in these environments, and their reliable detection is relevant to applications dealing with autonomous navigation, facility damage detection, city planning and maintenance. Yet, due to the characteristic thin shape, detection of man-made pole-like structures is significantly prone to both noise as well as occlusions and clutter, the latter being pervasive nuisances when scanning urban environments. Our approach is based on a ``local'' stage, whereby local features are classified and clustered together, followed by a ``global'' stage aimed at further classification of candidate entities. The proposed pipeline turns out effective in experiments on a standard publicly available dataset as well as on a challenging dataset acquired during the project for validation purposes.