Human Factors Evaluation of 360 Panoramic View Camera System for Ground Vehicle

2007 ◽  
Author(s):  
Cheng Li Wei ◽  
Ang Cher Wee ◽  
Chan Wai Herng ◽  
Ying Meng Fai
Keyword(s):  
Human Factors ◽  
Panoramic View ◽  
Camera System ◽  
Ground Vehicle

Human Factors Evaluation of 360 Panoramic View Camera System for Ground Vehicle

2007 ◽  
Vol 51 (18) ◽  
pp. 1086-1090
Author(s):  
Cheng Li Wei ◽  
Ang Cher Wee ◽  
Chan Wai Herng ◽  
Ying Meng Fai
Keyword(s):  
Situational Awareness ◽  
Field Of View ◽  
Graphic User Interface ◽  
Panoramic View ◽  
Camera System ◽  
Ground Vehicle ◽  
Operator Functions ◽  
Machine Interface ◽  
Commercial Off The Shelf ◽  
Horizontal View

The 360 Panoramic view camera System (360 PS) is a commercial-off-the-shelf camera technology that captures and presents the viewer with a 360-degree horizontal view around the camera. Due to its unique ability to monitor in omni-directional, there is potential for operator functions such as surveillance and monitoring, and for enhancing situational awareness of crews operating vehicles with restricted visual field-of-view. This man-in-the-loop evaluation is to study the limitations and capabilities of the system, and enhance its performance for the proposed functions. Two sets of experiments were designed, and carried out to study the man-machine interface (MMI) issues. Subjective and objective data were collected, which allowed us to identify two preferred display modes out of the six factory modes. Graphic user interface (GUI) overlays were then designed for these two display modes.

scholarly journals A human factors testbed for ground-vehicle telerobotics research

2002 ◽  
Author(s):  
G.K. Corbett ◽  
S.M. Killough ◽  
B.S. Richardson ◽  
M. Herman
Keyword(s):  
Human Factors ◽  
Ground Vehicle

Comparison of UGV Position Estimation Equipped With GNSS-RTK and GPS Using EKF

2020 ◽  
Author(s):  
Hesham Ismail ◽  
Thani Althani ◽  
Mohammed Minhas Anzil ◽  
Prashanth Subramaniam
Keyword(s):  
Extreme Temperature ◽  
Processing Technique ◽  
Position Estimation ◽  
Image Processing Technique ◽  
Measurement Unit ◽  
Unmanned Ground Vehicle ◽  
Camera System ◽  
Ground Vehicle ◽  
Positioning Device ◽  
Global Navigation Satellite

Abstract Site assessments for bifacial Photovoltaic (PV) installation are quite challenging to conduct manually due to the area size and the extreme temperature conditions at desert sites. We designed and built an autonomous Unmanned Ground Vehicle (UGV) fitted with a Global Navigation Satellite Network-System Real-Time Kinematic (GNSS-RTK) positioning device, an Inertial Measurement Unit (IMU), encoder to improve and aid site assessments in desert condition. Sandy terrains deserts are challenging for UGV’s because they increase the likelihood of wheel slippage due to reduced traction. Sensor details such as IMU, GNSS-RTK, and encoder should be taken into consideration to account for the errors that the desert terrains pose. This study compared the Extended Kalman Filter (EKF) for standard GPS & GNSS-RTK to verify which performs better for the UGV’s position estimation. The estimated UGV’s position from the kinematics model and EKF are validated using a drone camera system that uses an image processing technique to verify the UGV’s position with the help of the visible reference cones. Throughout the experiments, the GNSS-RTK performed better than GPS. Also, the EKF performed as well as the GNSS-RTK by trusting it more than the encoder/gyroscope reading.

scholarly journals Ford Campus vision and lidar data set

2011 ◽  
Vol 30 (13) ◽  
pp. 1543-1552 ◽  
Author(s):  
Gaurav Pandey ◽  
James R McBride ◽  
Ryan M Eustice
Keyword(s):  
Three Dimensional ◽  
Measurement Unit ◽  
Small Scale ◽  
Data Sets ◽  
Camera System ◽  
Data Set ◽  
Mapping Algorithms ◽  
Ground Vehicle ◽  
Localization And Mapping ◽  
Vehicle Path

In this paper we describe a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit, a Velodyne three-dimensional lidar scanner, two push-broom forward-looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research Campus and downtown Dearborn, MI, during November–December 2009. The vehicle path trajectory in these data sets contains several large- and small-scale loop closures, which should be useful for testing various state-of-the-art computer vision and simultaneous localization and mapping algorithms.

Precise on-line Measurement of Lattice Vectors

1990 ◽  
Vol 48 (1) ◽  
pp. 530-531
Author(s):  
W.J. de Ruijter ◽  
Sharma Renu
Keyword(s):  
Electron Microscope ◽  
Measurement Errors ◽  
Dynamic Range ◽  
Structural Information ◽  
Statistical Parameter ◽  
Systematic Errors ◽  
Geometric Distortion ◽  
Crystal Silicon ◽  
Camera System ◽  
On Line

Established methods for measurement of lattice spacings and angles of crystalline materials include x-ray diffraction, microdiffraction and HREM imaging. Structural information from HREM images is normally obtained off-line with the traveling table microscope or by the optical diffractogram technique. We present a new method for precise measurement of lattice vectors from HREM images using an on-line computer connected to the electron microscope. It has already been established that an image of crystalline material can be represented by a finite number of sinusoids. The amplitude and the phase of these sinusoids are affected by the microscope transfer characteristics, which are strongly influenced by the settings of defocus, astigmatism and beam alignment. However, the frequency of each sinusoid is solely a function of overall magnification and periodicities present in the specimen. After proper calibration of the overall magnification, lattice vectors can be measured unambiguously from HREM images.Measurement of lattice vectors is a statistical parameter estimation problem which is similar to amplitude, phase and frequency estimation of sinusoids in 1-dimensional signals as encountered, for example, in radar, sonar and telecommunications. It is important to properly model the observations, the systematic errors and the non-systematic errors. The observations are modelled as a sum of (2-dimensional) sinusoids. In the present study the components of the frequency vector of the sinusoids are the only parameters of interest. Non-systematic errors in recorded electron images are described as white Gaussian noise. The most important systematic error is geometric distortion. Lattice vectors are measured using a two step procedure. First a coarse search is obtained using a Fast Fourier Transform on an image section of interest. Prior to Fourier transformation the image section is multiplied with a window, which gradually falls off to zero at the edges. The user indicates interactively the periodicities of interest by selecting spots in the digital diffractogram. A fine search for each selected frequency is implemented using a bilinear interpolation, which is dependent on the window function. It is possible to refine the estimation even further using a non-linear least squares estimation. The first two steps provide the proper starting values for the numerical minimization (e.g. Gauss-Newton). This third step increases the precision with 30% to the highest theoretically attainable (Cramer and Rao Lower Bound). In the present studies we use a Gatan 622 TV camera attached to the JEM 4000EX electron microscope. Image analysis is implemented on a Micro VAX II computer equipped with a powerful array processor and real time image processing hardware. The typical precision, as defined by the standard deviation of the distribution of measurement errors, is found to be <0.003Å measured on single crystal silicon and <0.02Å measured on small (10-30Å) specimen areas. These values are ×10 times larger than predicted by theory. Furthermore, the measured precision is observed to be independent on signal-to-noise ratio (determined by the number of averaged TV frames). Obviously, the precision is restricted by geometric distortion mainly caused by the TV camera. For this reason, we are replacing the Gatan 622 TV camera with a modern high-grade CCD-based camera system. Such a system not only has negligible geometric distortion, but also high dynamic range (>10,000) and high resolution (1024x1024 pixels). The geometric distortion of the projector lenses can be measured, and corrected through re-sampling of the digitized image.

An Application of the HFACS Method to Aviation Accidents in Africa

2016 ◽  
Vol 6 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Isaac Munene
Keyword(s):  
Human Factors ◽  
Physical Environment ◽  
Human Factor ◽  
Flight Training ◽  
African Countries ◽  
Causal Factors ◽  
Aviation Accidents ◽  
Factors Analysis ◽  
Adverse Weather ◽  
Decision Errors

Abstract. The Human Factors Analysis and Classification System (HFACS) methodology was applied to accident reports from three African countries: Kenya, Nigeria, and South Africa. In all, 55 of 72 finalized reports for accidents occurring between 2000 and 2014 were analyzed. In most of the accidents, one or more human factors contributed to the accident. Skill-based errors (56.4%), the physical environment (36.4%), and violations (20%) were the most common causal factors in the accidents. Decision errors comprised 18.2%, while perceptual errors and crew resource management accounted for 10.9%. The results were consistent with previous industry observations: Over 70% of aviation accidents have human factor causes. Adverse weather was seen to be a common secondary casual factor. Changes in flight training and risk management methods may alleviate the high number of accidents in Africa.

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