Does the human brain represent objects for recognition by storing a series of two-dimensional snapshots, or are the object models, in some sense, three-dimensional analogs of the objects they represent? One way to address this question is to explore the ability of the human visual system to generalize recognition from familiar to unfamiliar views of threedimensional objects. Three recently proposed theories of object recognition-viewpoint normalization or alignment of threedimensional models [Ullman, S. (1989) [Poggio, T. & Edehnan, S. (1990) Nature (London) 343, 263-2661-predict different patterns of generalization to unfamiliar views. We have exploited the conflicting predictions to test the three theories directly in a psychophysical experiment involving computer-generated three-dimensional objects. Our results-suggest that the human visual system is better described as recognizing these objects by two-dimensional view interpolation than by alignment or other methods that rely on object-centered three-dimensional models.How does the human visual system represent objects for recognition? The experiments we describe address this question by testing the ability of human subjects (and of computer models instantiating particular theories of recognition) to generalize from familiar to unfamiliar views of visually novel objects. Because different theories predict different patterns of generalization according to the experimental conditions, this approach yields concrete evidence in favor of some ofthe theories and contradicts others. Theories That Rely on Three-Dimensional Object-Centered Representations The first class of theories we have considered (1-3) represents objects by three dimensional (3D) models, encoded in a viewpoint-independent fashion. One such approach, recognition by alignment (1), compares the input image with the projection of a stored model after the two are brought into register. The transformation necessary to achieve this registration is computed by matching a small number of features in the image with the corresponding features in the model. The aligning transformation is computed separately for each of the models stored in the system. Recognition is declared for the model that fits the input most closely after the two are aligned, if the residual dissimilarity between them is small enough. The decision criterion for recognition in this case can be stated in the following simplified form: 11PTx(3D) -x(2D)I < , [1] where T is the aligning transformation, P is a 3D twodimensional (2D) projection operator, and the norm IIu11 measures the dissimilarity between the projection of the transformed 3D model X(3D) and the input image X(2D). Recognition decision is then made based on a comparison between the measured dissimilarity and a threshold 6.One may make a further distinction between full alignment that uses 3D models and attempts to compensate for 3D transformations of objects (such as rotation in depth), and the alignment of pictorial descriptions that uses multiple views rather t...

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Standard quadrotor unmanned aerial vehicles (UAVs) possess a limited mobility because of their inherent underactuation, that is, availability of four independent control inputs (the four propeller spinning velocities) versus the 6 degrees of freedom parameterizing the quadrotor position/orientation in space. Thus, the quadrotor pose cannot track arbitrary trajectories in space (e.g., it can hover on the spot only when horizontal). Because UAVs are more and more employed as service robots for interaction with the environment, this loss of mobility due to their underactuation can constitute a limiting factor. In this paper, we present a novel design for a quadrotor UAV with tilting propellers which is able to overcome these limitations. Indeed, the additional set of four control inputs actuating the propeller tilting angles is shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully actuated flying vehicle. We then develop a comprehensive modeling and control framework for the proposed quadrotor, and subsequently illustrate the hardware and software specifications of an experimental prototype. Finally, the results of several simulations and real experiments are reported to illustrate the capabilities of the proposed novel UAV design

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Abstract-We propose a novel semi-autonomous haptic teleoperation control architecture for multiple unmanned aerial vehicles (UAVs), consisting of three control layers: 1) UAV control layer, where each UAV is abstracted by, and is controlled to follow the trajectory of, its own kinematic Cartesian virtual point (VP); 2) VP control layer, which modulates each VP's motion according to the teleoperation commands and local artificial potentials (for VP-VP/VP-obstacle collision avoidance and VP-VP connectivity preservation); and 3) teleoperation layer, through which a single remote human user can command all (or some) of the VPs' velocity while haptically perceiving the state of all (or some) of the UAVs and obstacles. Master-passivity/slave-stability and some asymptotic performance measures are proved. Experimental results using four custom-built quadrotor-type UAVs are also presented to illustrate the theory.

Abstract-Standard quadrotor UAVs possess a limited mobility because of their inherent underactuation, i.e., availability of 4 independent control inputs (the 4 propeller spinning velocities) vs. the 6 dofs parameterizing the quadrotor position/orientation in space. As a consequence, the quadrotor pose cannot track an arbitrary trajectory over time (e.g., it can hover on the spot only when horizontal). In this paper, we propose a novel actuation concept in which the quadrotor propellers are allowed to tilt about their axes w.r.t. the main quadrotor body. This introduces an additional set of 4 control inputs which provides full actuation to the quadrotor position/orientation. After deriving the dynamical model of the proposed quadrotor, we formally discuss its controllability properties and propose a nonlinear trajectory tracking controller based on dynamic feedback linearization techniques. The soundness of our approach is validated by means of simulation results.

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The interaction between depth perception and object recognition has important implications for the nature of mental object representations and models of hierarchical organization of visual processing. It is often believed that the computation of depth influences subsequent high-level object recognition processes, and that depth processing is an early vision task that is largely immune to 'top-down' object-specific influences, such as object recognition. Here we present experimental evidence that challenges both these assumptions in the specific context of stereoscopic depth-perception. We have found that observers' recognition of familiar dynamic three-dimensional (3D) objects is unaffected even when the objects' depth structure is scrambled, as long as their two-dimensional (2D) projections are unchanged. Furthermore, the observers seem perceptually unaware of the depth anomalies introduced by scrambling. We attribute the latter result to a top-down recognition-based influence whereby expectations about a familiar object's 3D structure override the true stereoscopic information.