The problem of finding a next best viewpoint for 3D modeling or scene mapping has been explored in computer vision over the last decade. This paper tackles a similar problem, but with different characteristics. It proposes a method for dynamic next best viewpoint recovery of a target point while avoiding possible occlusions. Since the environment can change, the method has to iteratively find the next best view with a global understanding of the free and occupied parts. We model the problem as a set of possible viewpoints which correspond to the centers of the facets of a virtual tessellated hemisphere covering the scene. Taking into account occlusions, distances between current and future viewpoints, quality of the viewpoint and joint constraints (robot arm joint distances or limits), we evaluate the next best viewpoint. The proposal has been evaluated on 8 different scenarios with different occlusions and a short 3D video sequence to validate its dynamic performance.
The dynamic visual tracking problem is a completely different problem to NBV. A camera mounted on a moving or stationary base is used to track an object. In @cite_6 the authors defined visual tracking in 2D of a single feature point as the translation @math and rotation @math with respect to the camera frame that keeps @math stationary, where @math is the area in the image plane where the target is projected. Many authors e.g. @cite_6 @cite_8 have addressed this problem using different approaches.
@cite_6 @cite_8
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The authors present algorithms for robotic (eye-in-hand configuration) real-time visual tracking of arbitrary 3D objects traveling at unknown velocities in a 2D space (depth is given as known). Visual tracking is formulated as a problem of combining control with computer vision. A mathematical formulation of the control problem that includes information from a novel feedback vision sensor and represents everything with respect to the camera frame is presented. The sum-of-squared differences (SSD) optical flow is used to compute the vector of discrete displacements each instant of time. These displacements can be fed either directly to a PI (proportional-integral) controller or to a pole assignment controller or discrete steady-state Kalman filter. In the latter case, the Kalman filter calculates the estimated values of the system's states and the exogenous disturbances, and a discrete LQG (linear-quadratic Gaussian) controller computes the desired motion of the robotic system. The outputs of the controllers are sent to the Cartesian robotic controller. Performance results are presented. > Abstract : This work represents a more general approach to robotic system design than one based on predefined responses in a controlled environment. An implementing of a vision-based robotic tracking system is presented in which target trajectory predictions enable the robot to track and intercept a moving target. A host microcomputer receives target position information from a vision module, predicts the target's trajectory, and issues tracking commands to the robot controlled. Five predictive algorithms are derived for implementation in the system, including a Kalman and an augmented Kalman filter. The use of one-step as well as absolute and relative n-step predictions is investigated. The best predictor algorithm is presented, by which one of the five predictions is selected to be used as the robotic tracking command. Using data from experimental trials, predictor results are compared and robotic tracking performance and interception success are evaluated for the target both moving and after it comes to rest. Constraints limiting the applicability of this implementation are discussed and possible improvements and extensions suggested. (Author)