Automatic Mapping with Obstacle Identification for Indoor Human Mobility Assessment
This work addresses indoor accessibility for people with limited mobility, but it is incremental as it combines existing SLAM and object recognition methods.
The paper tackles the problem of assessing indoor mobility hazards by developing a framework that uses a mobile robot to create a map and identify obstacles for people with limited mobility, resulting in a detailed survey of object locations and bounding volumes.
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.