Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Spherical shell: a higher order bounding volume for fast proximity queries

Abstract: Hierarchical data structures have been widely used to design e cient algorithms for interference detection for robot motion planning and physically-based modeling applications. Most of the hierarchies involve use of bounding volumes which enclose the underlying geometry. These bounding volumes are used to test for interference or compute distance bounds between the underlying geometry. The e ciency of a hierarchy is directly proportional to the choice of a bounding volume. In this paper, we introduce spherical shells, a higher order bounding volume for fast proximity queries. Each shell corresponds to a portion of the volume between two concentric spheres. We present algorithms to compute tight tting shells and fast overlap between two shells. Moreover, we show that spherical shells provide local cubic convergence to the underlying geometry. As a result, in many cases they provide faster algorithms for interference detection and distance computation as compared to earlier methods. We also describe an implementation and compare it with other hierarchies.

Locating Objects Without Bounding Boxes

Abstract: Recent advances in convolutional neural networks (CNN) have achieved remarkable results in locating objects in images. In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects. In this paper, we address the task of estimating object locations without annotated bounding boxes which are typically hand-drawn and time consuming to label. We propose a loss function that can be used in any fully convolutional network (FCN) to estimate object locations. This loss function is a modification of the average Hausdorff distance between two unordered sets of points. The proposed method has no notion of bounding boxes, region proposals, or sliding windows. We evaluate our method with three datasets designed to locate people’s heads, pupil centers and plant centers. We outperform state-of-the-art generic object detectors and methods fine-tuned for pupil tracking.

Providing end-to-end statistical performance guarantees with bounding interval dependent stochastic models

Abstract: This paper demonstrates a new, efficient, and general approach for providing end-to-end performance guarantees in integrated services networks. This is achieved by modeling a traffic source with a family of bounding interval-dependent (BIND) random variables and by using a rate-controlled service discipline inside the network. The traffic model stochastically bounds the number of bits sent over time intervals of different length. The model captures different source behavior over different time scales by making the bounding distribution an explicit function of the interval length. The service discipline, RCSP, has the priority queueing mechanisms necessary to provide performance guarantees in integrated services networks. In addition, RCSP provides the means for efficiently extending the results from a single switch to a network of arbitrary topology. These techniques are derived analytically and then demonstrated with numerical examples.

Localization of a mobile robot using relative bearing measurements

Abstract: In this paper, the problem of recursive robot localization based on relative bearing measurements is considered, where unknown but bounded measurement uncertainties are assumed. A common approach is to approximate the resulting set of feasible states by simple-shaped bounding sets such as, e.g., axis-aligned boxes, and calculate the optimal parameters of this approximation based on the measurements and prior knowledge. In the novel approach presented here, a nonlinear transformation of the measurement equation into a higher dimensional space is performed. This yields a tight, possibly complex-shaped, bounding set in a closed-form representation whose parameters can be determined analytically for the measurement step. It is shown that the new bound is superior to commonly used outer bounds.