  We propose a framework that allows a mobile robot to build a map of an indoor
scenario, identifying and highlighting objects that may be considered a
hindrance to people with limited mobility. The map is built by combining recent
developments in monocular SLAM with information from inertial sensors of the
robot platform, resulting in a metric point cloud that can be further processed
to obtain a mesh. The images from the monocular camera are simultaneously
analyzed with an object recognition neural network, tuned to detect a
particular class of targets. This information is then processed and
incorporated on the metric map, resulting in a detailed survey of the locations
and bounding volumes of the objects of interest. The result can be used to
inform policy makers and users with limited mobility of the hazards present in
a particular indoor location. Our initial tests were performed using a
micro-UAV and will be extended to other robotic platforms.
