Abstract: In this paper, propose method that can user’s position
that based on database is built from single camera. Previous
positioning calculate distance by arrival-time of signal like GPS
(Global Positioning System), RF(Radio Frequency). However, these
previous method have weakness because these have large error range
according to signal interference. Method for solution estimate position
by camera sensor. But, signal camera is difficult to obtain relative
position data and stereo camera is difficult to provide real-time
position data because of a lot of image data, too. First of all, in this
research we build image database at space that able to provide
positioning service with single camera. Next, we judge similarity
through image matching of database image and transmission image
from user. Finally, we decide position of user through position of most
similar database image. For verification of propose method, we
experiment at real-environment like indoor and outdoor. Propose
method is wide positioning range and this method can verify not only
position of user but also direction.
Abstract: Lately, with the increasing number of location-based applications, demand for highly accurate and reliable indoor localization became urgent. This is a challenging problem, due to the measurement variance which is the consequence of various factors like obstacles, equipment properties and environmental changes in complex nature of indoor environments. In this paper we propose low-cost custom-setup infrastructure solution and localization algorithm based on the Weighted Centroid Localization (WCL) method. Localization accuracy is increased by several enhancements: calibration of RSSI values gained from wireless nodes, repetitive measurements of RSSI to exclude deviating values from the position estimation, and by considering orientation of the device according to the wireless nodes. We conducted several experiments to evaluate the proposed algorithm. High accuracy of ~1m was achieved.
Abstract: This paper presents the novel Rao-Blackwellised
particle filter (RBPF) for mobile robot simultaneous localization and
mapping (SLAM) using monocular vision. The particle filter is
combined with unscented Kalman filter (UKF) to extending the path
posterior by sampling new poses that integrate the current observation
which drastically reduces the uncertainty about the robot pose. The
landmark position estimation and update is also implemented through
UKF. Furthermore, the number of resampling steps is determined
adaptively, which seriously reduces the particle depletion problem,
and introducing the evolution strategies (ES) for avoiding particle
impoverishment. The 3D natural point landmarks are structured with
matching Scale Invariant Feature Transform (SIFT) feature pairs. The
matching for multi-dimension SIFT features is implemented with a
KD-Tree in the time cost of O(log2
N). Experiment results on real robot
in our indoor environment show the advantages of our methods over
previous approaches.
Abstract: Virtual touch screen using camera is an ordinary screen which uses a camera to imitate the touch screen by taking a picture of an indicator, e.g., finger, which is laid on the screen, converting the indicator tip position on the picture to the position on the screen, and moving the cursor on the screen to that position. In fact, the indicator is not laid on the screen directly, but it is intervened by the cover at some intervals. In spite of this gap, if the eye-indicator-camera angle is not large, the mapping from the indicator tip positions on the image to the corresponding cursor positions on the screen is not difficult and could be done with a little error. However, the larger the angle is, the bigger the error in the mapping occurs. This paper proposes cursor position estimation model for virtual touch screen using camera which could eliminate this kind of error. The proposed model (i) moves the on-screen pilot cursor to the screen position which locates on the screen at the position just behind the indicator tip when the indicator tip has been looked from the camera position, and then (ii) converts that pilot cursor position to the desirable cursor position (the position on the screen when it has been looked from the user-s eye through the indicator tip) by using the bilinear transformation. Simulation results show the correctness of the estimated cursor position by using the proposed model.
Abstract: We present a system that finds road boundaries and
constructs the virtual lane based on fusion data from a laser and a
monocular sensor, and detects forward vehicle position even in no lane
markers or bad environmental conditions. When the road environment
is dark or a lot of vehicles are parked on the both sides of the road, it is
difficult to detect lane and road boundary. For this reason we use
fusion of laser and vision sensor to extract road boundary to acquire
three dimensional data. We use parabolic road model to calculate road
boundaries which is based on vehicle and sensors state parameters and
construct virtual lane. And then we distinguish vehicle position in each
lane.
Abstract: The adaptive power control of Code Division Multiple
Access (CDMA) communications using Remote Radio Head
(RRH) between multiple Unmanned Aerial Vehicles (UAVs) with
a link-budget based Signal-to-Interference Ratio (SIR) estimate is
applied to four inner loop power control algorithms. It is concluded
that Base Station (BS) can calculate not only UAV distance using
linearity between speed and Consecutive Transmit-Power-Control
Ratio (CTR) of Adaptive Step-size Closed Loop Power Control (ASCLPC),
Consecutive TPC Ratio Step-size Closed Loop Power Control
(CS-CLPC), Fixed Step-size Power Control (FSPC), but also UAV
position with Received Signal Strength Indicator (RSSI) ratio of
RRHs.
Abstract: In recent years a number of applications with multirobot
systems (MRS) is growing in various areas. But their design
is in practice often difficult and algorithms are proposed for the
theoretical background and do not consider errors and noise in real
conditions, so they are not usable in real environment. These errors
are visible also in task of target localization enough, when robots
try to find and estimate the position of the target by the sensors.
Localization of target is possible also with one robot but as it was
examined target finding and localization with group of mobile robots
can estimate the target position more accurately and faster. The
accuracy of target position estimation is made by cooperation of
MRS and particle filtering. Advantage of usage the MRS with particle
filtering was tested on task of fixed target localization by group of
mobile robots.
Abstract: In the present study, position estimation of switched reluctance motor (SRM) has been achieved on the basis of the artificial neural networks (ANNs). The ANNs can estimate the rotor position without using an extra rotor position sensor by measuring the phase flux linkages and phase currents. Flux linkage-phase current-rotor position data set and supervised backpropagation learning algorithm are used in training of the ANN based position estimator. A 4-phase SRM have been used to verify the accuracy and feasibility of the proposed position estimator. Simulation results show that the proposed position estimator gives precise and accurate position estimations for both under the low and high level reference speeds of the SRM