1,721,078 research outputs found
Scalable Dense Large-Scale Mapping and Navigation
No full textThis paper describes a scalable dense 3D recon- struction and navigation system suitable for real-time operation. The system represents the environment as the back-projection of a Delaunay triangulation of the omnidirectional image, estimated at each instant from two adjacent views. The cost being minimized (i.e., the reprojection error) is photometric rather than geometric, as in the majority of feature-based reconstruction and navigation systems. While temporal inte- gration would enable more accurate reconstruction, this would carry the computational burden of handling topological changes due to occlusion phenomena. We successfully tested our system in a challenging urban scenario along a large loop using an omnidirectional camera mounted on the roof of a car
Observability Linear Hybrid Systems
No full textWe analyze the observability of the continuous and discrete
states of continuous-time linear hybrid systems. For the class of jumplinear
systems, we derive necessary and sucient conditions that the
structural parameters of the model must satisfy in order for ltering and
smoothing algorithms to operate correctly. Our conditions are simple
rank tests that exploit the geometry of the observability subspaces. For
linear hybrid systems, we derive weaker rank conditions that are sucient
to guarantee the uniqueness of the reconstruction of the state trajectory,
even when the individual linear systems are unobservable
Cataloging Birds in Their Natural Habitat
No full textWe devise a method to catalog novel objects from an image sequence even when the underlying scene exhibits sudden motion and appearance changes in consecutive frames, exemplified by the case of birds in their natural habitat. Cataloging birds in different ecosystems can provide important measures towards scientific models of global warming. However, images captured of the natural environment exhibit many visual “nuisances” that challenge standard detection and tracking methods that would allow for the cataloging of birds. We propose a method that specifically models the finescaled changes on the background due to motion, selfocclusion, and lighting changes. Regions that do not fit in this model are considered an instance of some bird. We then associate these regions with bird identities by allowing for either appearance similarity or location proximity as a guide. Birds are then clustered into visually similar groups that approximate species. Experiments show that we can maintain tracks for significantly longer periods of time as compared to classic mean shift tracking, and provide meaningful clusters for the end user
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