This work proposes a strategy for autonomous change detection and classification using aerial robots. For aerial robotic missions that were conducted in different spatio-temporal conditions, the pose-annotated camera data are first compared for similarity in order to identify the correspondence map among the different image sets. Then efficient feature matching techniques relying on binary descriptors are used to estimate the geometric transformations among the corresponding images, and subsequently perform image subtraction and filtering to robustly detect change. To further decrease the computational load, the known poses of the images are used to create local subsets within which similar images are expected to be found. Once change detection is accomplished, a small set of the images that present the maximum levels of change are used to classify the change by searching to recognize a list of known objects through a bag-of-features approach. The proposed algorithm is evaluated using both handheld-smartphone collected data, as well as experiments using an aerial robot.
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