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The direct use of the entire photometric image information as dense features for visual servoing brings several advantages. First, it does not require any feature detection, matching, or tracking process. Thanks to the redundancy of visual information, the precision at convergence is highly accurate. However, the corresponding highly nonlinear cost function reduces the convergence domain. In this paper, we propose visual servoing based on the analytical formulation of Gaussian mixtures to enlarge the convergence domain. Pixels are represented by two-dimensional Gaussian functions that denote a “power of attraction.” In addition to the control of the camera velocities during the servoing, we also optimize the Gaussian spreads allowing the camera to precisely converge to a desired pose even from a far initial one. Simulations show that our approach outperforms the state of the art and real experiments show the effectiveness, robustness, and accuracy of our approach. [ABSTRACT FROM AUTHOR]

In this paper, visual servoing based on photometric moments is advocated. A direct approach is chosen by which the extraction of geometric primitives, visual tracking and image matching steps of a conventional visual servoing pipeline can be bypassed. A vital challenge in photometric methods is the change in the image resulting from the appearance and disappearance of portions of the scene from the camera field of view during the servo. To tackle this issue, a general model for the photometric moments enhanced with spatial weighting is proposed. The interaction matrix for these spatially weighted photometric moments is derived in an analytical form. The correctness of the modeling, effectiveness of the proposed strategy in handling the exogenous regions, and improved convergence domain are demonstrated with a combination of simulation and the experimental results. [ABSTRACT FROM AUTHOR]

The determination of the singularity cases in visual servoing is a tricky problem, which is unsolved for most of the image-based approaches. In order to avoid singularities, redundant measurements may be used. However, they lead to the presence of local minima. Moreover, they do not always ensure that singularities can be avoided. Here, we show that a concept named the “hidden robot,” which was formerly used for understanding the singularities of a vision-based controller dedicated to parallel robots, can be used for interpreting the singularities in the visual servoing of image points. These singularity cases were already found in the case in which three points are observed, but we show that the hidden robot concept considerably simplifies the analysis by using geometric interpretations of the mapping degeneracy and tools provided by the mechanical engineering community. Moreover, to the best of our knowledge, for the first time, we provide the singularity conditions when more than three points are observed. We also discuss how these tools could be extended in order to find the singularity cases of other visual servoing techniques (e.g., when lines are observed). [ABSTRACT FROM PUBLISHER]

Moving obstacle avoidance is a fundamental requirement for any robot operating in real environments, where pedestrians, bicycles, and cars are present. In this paper, we propose and validate a framework for avoiding moving obstacles during visual navigation with a wheeled mobile robot. Visual navigation consists of following a path, represented as an ordered set of key images, which have been acquired by an on-board camera in a teaching phase. While following such a path, our robot is able to avoid static and moving obstacles, which were not present during teaching, and which are sensed by an on-board lidar. The proposed approach takes explicitly into account obstacle velocities, estimated using an appropriate Kalman-based observer. The velocities are then used to predict the obstacle positions within a tentacle-based approach. Finally, our approach is validated in a series of real outdoor experiments, showing that when the obstacle velocities are considered, the robot behavior is safer, smoother, and faster than when it is not. [ABSTRACT FROM PUBLISHER]