3D Disaster Scene Reconstruction Using a Canine-Mounted RGB-D Sensor

A 3D map of the interior of a disaster site that pinpoints the location of trapped victims would greatly aid search and rescue efforts. We propose using a canine-mounted RGB-D sensor; a trained rescue dog can carry an image sensor through the site to build a 3D model useful for rescuers. However, the registration of the data provides challenges beyond those typically faced in scene reconstruction due to the rapid motion and sudden pose changes. We provide a solution whereby a pre-processing step identifies good frames to combine from a stream of RGB-D image frames. These selected images are then combined into the larger model by calculating a relative pose using the 3D location of key points matched in the visible images. Results are presented of 3D models constructed using data collected from the canine platform.