55,616 research outputs found
Robust Algorithms for Object Localization
Object localization using sensed data features and corresponding model features is a fundamental problem in machine vision. We reformulate object localization as a least squares problem: the optimal pose estimate minimizes the squared error (discrepancy) between the sensed and predicted data. The resulting problem is non-linear and previous attempts to estimate the optimal pose using local methods such as gradient descent suffer from local minima and, at times, return incorrect results. In this paper, we describe an exact, accurate and efficient algorithm based on resultants, linear algebra, and numerical analysis, for solving the nonlinear least squares problem associated with localizing two-dimensional objects given two-dimensional data. This work is aimed at tasks where the sensor features and the model features are of different types and where either the sensor features or model features are points. It is applicable to localizing modeled objects from image data, and estimates the pose using all of the pixels in the detected edges. The algorithm's running time depends mainly on the type of non-point features, and it also depends to a small extent on the number of features. On a SPARC 10, the algorithm takes a few microseconds for rectilinear features, a few milliseconds for linear features, and a few seconds for circular features
Power Optimization for Network Localization
Reliable and accurate localization of mobile objects is essential for many
applications in wireless networks. In range-based localization, the position of
the object can be inferred using the distance measurements from wireless
signals exchanged with active objects or reflected by passive ones. Power
allocation for ranging signals is important since it affects not only network
lifetime and throughput but also localization accuracy. In this paper, we
establish a unifying optimization framework for power allocation in both active
and passive localization networks. In particular, we first determine the
functional properties of the localization accuracy metric, which enable us to
transform the power allocation problems into second-order cone programs
(SOCPs). We then propose the robust counterparts of the problems in the
presence of parameter uncertainty and develop asymptotically optimal and
efficient near-optimal SOCP-based algorithms. Our simulation results validate
the efficiency and robustness of the proposed algorithms.Comment: 15 pages, 7 figure
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Localization from semantic observations via the matrix permanent
Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization
Micro-Net: A unified model for segmentation of various objects in microscopy images
Object segmentation and structure localization are important steps in
automated image analysis pipelines for microscopy images. We present a
convolution neural network (CNN) based deep learning architecture for
segmentation of objects in microscopy images. The proposed network can be used
to segment cells, nuclei and glands in fluorescence microscopy and histology
images after slight tuning of input parameters. The network trains at multiple
resolutions of the input image, connects the intermediate layers for better
localization and context and generates the output using multi-resolution
deconvolution filters. The extra convolutional layers which bypass the
max-pooling operation allow the network to train for variable input intensities
and object size and make it robust to noisy data. We compare our results on
publicly available data sets and show that the proposed network outperforms
recent deep learning algorithms
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
The World of Fast Moving Objects
The notion of a Fast Moving Object (FMO), i.e. an object that moves over a
distance exceeding its size within the exposure time, is introduced. FMOs may,
and typically do, rotate with high angular speed. FMOs are very common in
sports videos, but are not rare elsewhere. In a single frame, such objects are
often barely visible and appear as semi-transparent streaks.
A method for the detection and tracking of FMOs is proposed. The method
consists of three distinct algorithms, which form an efficient localization
pipeline that operates successfully in a broad range of conditions. We show
that it is possible to recover the appearance of the object and its axis of
rotation, despite its blurred appearance. The proposed method is evaluated on a
new annotated dataset. The results show that existing trackers are inadequate
for the problem of FMO localization and a new approach is required. Two
applications of localization, temporal super-resolution and highlighting, are
presented
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