143,096 research outputs found

    Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data

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    Localization is a key requirement for mobile robot autonomy and human-robot interaction. Vision-based localization is accurate and flexible, however, it incurs a high computational burden which limits its application on many resource-constrained platforms. In this paper, we address the problem of performing real-time localization in large-scale 3D point cloud maps of ever-growing size. While most systems using multi-modal information reduce localization time by employing side-channel information in a coarse manner (eg. WiFi for a rough prior position estimate), we propose to inter-weave the map with rich sensory data. This multi-modal approach achieves two key goals simultaneously. First, it enables us to harness additional sensory data to localise against a map covering a vast area in real-time; and secondly, it also allows us to roughly localise devices which are not equipped with a camera. The key to our approach is a localization policy based on a sequential Monte Carlo estimator. The localiser uses this policy to attempt point-matching only in nodes where it is likely to succeed, significantly increasing the efficiency of the localization process. The proposed multi-modal localization system is evaluated extensively in a large museum building. The results show that our multi-modal approach not only increases the localization accuracy but significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots (Humanoids) 201

    Cramer-Rao Bounds for Joint RSS/DoA-Based Primary-User Localization in Cognitive Radio Networks

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    Knowledge about the location of licensed primary-users (PU) could enable several key features in cognitive radio (CR) networks including improved spatio-temporal sensing, intelligent location-aware routing, as well as aiding spectrum policy enforcement. In this paper we consider the achievable accuracy of PU localization algorithms that jointly utilize received-signal-strength (RSS) and direction-of-arrival (DoA) measurements by evaluating the Cramer-Rao Bound (CRB). Previous works evaluate the CRB for RSS-only and DoA-only localization algorithms separately and assume DoA estimation error variance is a fixed constant or rather independent of RSS. We derive the CRB for joint RSS/DoA-based PU localization algorithms based on the mathematical model of DoA estimation error variance as a function of RSS, for a given CR placement. The bound is compared with practical localization algorithms and the impact of several key parameters, such as number of nodes, number of antennas and samples, channel shadowing variance and correlation distance, on the achievable accuracy are thoroughly analyzed and discussed. We also derive the closed-form asymptotic CRB for uniform random CR placement, and perform theoretical and numerical studies on the required number of CRs such that the asymptotic CRB tightly approximates the numerical integration of the CRB for a given placement.Comment: 20 pages, 11 figures, 1 table, submitted to IEEE Transactions on Wireless Communication

    Subjective evaluation of an emerging theory of low-frequency sound source localization in closed acoustic spaces

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    An earlier reported theory of low-frequency sound-source localization within closed acoustic spaces proposed that virtual image acuity is strongly dependent on sufficient inter-arrival time between a direct sound and its first reflection. This current study aims to test the theory’s predictions by subjective experiment where participants are required to indicate perceived sound source direction, but without knowledge of loudspeaker location. Test signals of frequencies 40 Hz to 115 Hz take the form of either windowed sine or square waves. Results confirm broad agreement with theoretical expectations and support the conjecture, contrary to common expectation, that low-frequency sound localization within the context of closed acoustic spaces is possible, although strongly dependent on system configuration and size of a listening space

    A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry

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    The demand for autonomous vehicles is increasing gradually owing to their enormous potential benefits. However, several challenges, such as vehicle localization, are involved in the development of autonomous vehicles. A simple and secure algorithm for vehicle positioning is proposed herein without massively modifying the existing transportation infrastructure. For vehicle localization, vehicles on the road are classified into two categories: host vehicles (HVs) are the ones used to estimate other vehicles' positions and forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV transmits modulated data from the tail (or back) light, and the camera of the HV receives that signal using optical camera communication (OCC). In addition, the streetlight (SL) data are considered to ensure the position accuracy of the HV. Determining the HV position minimizes the relative position variation between the HV and FV. Using photogrammetry, the distance between FV or SL and the camera of the HV is calculated by measuring the occupied image area on the image sensor. Comparing the change in distance between HV and SLs with the change in distance between HV and FV, the positions of FVs are determined. The performance of the proposed technique is analyzed, and the results indicate a significant improvement in performance. The experimental distance measurement validated the feasibility of the proposed scheme

    Development of a tabletop guidance system for educational robots

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    The guidance of a vehicle in an outdoor setting is typically implemented using a Real Time Kinematic Global Positioning System (RTK-GPS) potentially enhanced by auxiliary sensors such as electronic compasses, rotation encoders, gyroscopes, and vision systems. Since GPS does not function in an indoor setting where educational competitions are often held, an alternative guidance system was developed. This article describes a guidance method that contains a laser-based localization system, which uses a robot-borne single laser transmitter spinning in a horizontal plane at an angular velocity up to 81 radians per second. Sensor arrays positioned in the corners of a flat rectangular table with dimensions of 1.22 m × 1.83 m detected the laser beam passages. The relative time differences among the detections of the laser passages gave an indication of the angles of the sensors with respect to the laser beam transmitter on the robot. These angles were translated into Cartesian coordinates. The guidance of the robot was implemented using a uni-directional wireless serial connection and position feedback from the localization system. Three experiments were conducted to test the system: 1) the accuracy of the static localization system was determined while the robot stood still. In this test the average error among valid measurements was smaller than 0.3 %. However, a maximum of 3.7 % of the measurements were invalid due to several causes. 2) The accuracy of the guidance system was assessed while the robot followed a straight line. The average deviation from this straight line was 3.6 mm while the robot followed a path with a length of approximately 0.9 m. 3) The overall performance of the guidance system was studied while the robot followed a complex path consisting of 33 sub-paths. The conclusion was that the system worked reasonably accurate, unless the robot came in close proximity

    High precision real-time location estimates in a real-life barn environment using a commercial ultra wideband chip

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    Structural changes lead to an increase in the number of dairy cows and dry sows kept per group. This has consequences in how easily a farmer can supervise his herd and may be detrimental to animal welfare, specifically regarding social relations, time budget and area of residence. An automated tracking system can support the farmer in his management activities and can provide the foundation for a scientific assessment of the welfare consequences of large groups. In this study, a relatively simple and inexpensive real time location system (RTLS) was developed with the aim of achieving precise localization of several tags (animals) in real time and in a real barn environment. The RTLS was based on the ultra-wideband (UWB) technology provided by DecaWave and was adapted for a time difference of arrival (TDoA) procedure to estimate the tags’ positions. The RTLS can handle up to a hundred tags simultaneously using a Pure ALOHA random access method at 1-second intervals. The localization of the tags was estimated in 2D on a given fixed height using a constrained Gauss-Newton algorithm to increase accuracy and stability. The performance of the overall system was evaluated in two different dairy barns. To determine the precision of the system, static and dynamic positions measured at withers height of a cow (1.5 m) and closer to the ground mimicking a lying cow were compared with a reference system (theodolite). The 2D deviations between the systems were used as a measure of precision. In addition, the scalability in respect to the number of tags and the size of the observed area was examined in situations with ten tags and the situation with 100 tags was simulated with a ten-fold increase in sampling rate. According to the field test, the system as developed can be used for the individual localization of animals. At withers height, most of the measured locations deviated less than 0.5 m from the localizations as measured by the theodolite. At lower heights, and closer to the corners of the observed area, some localization estimates were somewhat larger. This was also the case close to large metal barn infrastructure. The measured collision rate of 11% for 100 tags was low. In spite of its low price, the system as a whole is therefore promising and ready for a next step, which should include the observation of large groups of real animals on working farms

    Hybrid 3D Localization for Visible Light Communication Systems

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    In this study, we investigate hybrid utilization of angle-of-arrival (AOA) and received signal strength (RSS) information in visible light communication (VLC) systems for 3D localization. We show that AOA-based localization method allows the receiver to locate itself via a least squares estimator by exploiting the directionality of light-emitting diodes (LEDs). We then prove that when the RSS information is taken into account, the positioning accuracy of AOA-based localization can be improved further using a weighted least squares solution. On the other hand, when the radiation patterns of LEDs are explicitly considered in the estimation, RSS-based localization yields highly accurate results. In order to deal with the system of nonlinear equations for RSS-based localization, we develop an analytical learning rule based on the Newton-Raphson method. The non-convex structure is addressed by initializing the learning rule based on 1) location estimates, and 2) a newly developed method, which we refer as random report and cluster algorithm. As a benchmark, we also derive analytical expression of the Cramer-Rao lower bound (CRLB) for RSS-based localization, which captures any deployment scenario positioning in 3D geometry. Finally, we demonstrate the effectiveness of the proposed solutions for a wide range of LED characteristics and orientations through extensive computer simulations.Comment: Submitted to IEEE/OSA Journal of Lightwave Technology (10 pages, 14 figures
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