Browsing by Subject "Extended Kalman Filter"

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    Estimation of object location and radius of curvature using ultrasonic sonar
    (Elsevier, 2001-07) Sekmen, A. Ş.; Barshan, B.
    Acoustic sensors are very popular in time-of-flight (TOF) ranging systems since they are inexpensive and convenient to use. One of the major limitations of these sensors is their low angular resolution which makes object localization difficult. In this paper, an adaptive multisensor configuration consisting of three transmitter/receiver ultrasonic transducers is introduced to compensate for the low angular resolution of sonar sensors and improve the localization accuracy. With this configuration, the radius of curvature and location of cylindrical objects are estimated. Two methods of TOF estimation are considered: thresholding and curve-fitting. The bias-variance combinations of these estimators are compared. Theory and simulations are verified by experimental data from a real sonar system. Extended Kalman filtering is used to smooth the data. It is shown that curve-fitting method, compared to thresholding method, provides about 30% improvement in the absence of noise and 50% improvement in the presence of noise. Moreover. the adaptive configuration improves the estimation accuracy by 35-40%. (C) 2001 Elsevier Science Ltd. All rights reserved.
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    Simultaneous localization and mapping for unmanned aerial vehicles
    (2008) Kök, Mehmet
    Most mobile robot applications require the robot to be able to localize itself in an unknown environment without prior information so that the robot can navigate and accomplish tasks. The robot must be able to build a map of the unknown environment while simultaneously localizing itself in this environment. The Simultaneous Localization and Mapping (SLAM) is the formulation of this problem which has drawn a considerable amount of interest in robotics research for the past two decades. This work focuses on the SLAM problem for single and multiple agents equipped with vision sensors. We develop a vision-based 2-D SLAM algorithm for single and multiple Unmanned Aerial Vehicles (UAV) flying at constant altitude. Using the features of images obtained from an on-board camera to identify different landmarks, we apply different approaches based on the Extended Kalman Filter (EKF), the Information Filter (IF) and the Particle Filter (PF) to the SLAM problem. We present some simulation results and provide a comparison between the different implementations. We find Particle Filter implementations to perform better in estimations when compared to EKF and IF, however EKF and IF present more consistent results.