Khalil Mustafa Ahmad Yousef

Building 3D visual maps of interior space with a new hierarchical sensor-fusion architecture

Abstract It is now generally recognized that sensor-fusion is the best approach to the accurate construction of environment maps by a sensor-equipped mobile robot. Typically, range data collected with a range sensor is combined with the reflectance data obtained from one or more cameras mounted on the robot. In much of the past work on sensor fusion in hierarchical approaches to map construction, the fusion was carried out only at the lowest level of the hierarchy. As a result, in those approaches, only the fused data was made available to the higher levels in the hierarchy. This implied that any errors caused by sensor fusion would propagate upwards into the higher level representations of an interior map. Our work, on the other hand, checks for consistency between the data elements produced by the different sensors at all levels of the hierarchy. This consistency checking is carried out with the help of an interval-based representation of uncertainties in the sensor data. In addition to demonstrating that our approach to the fusion of range and image data results in dense 3D maps of the interior space, we also provide validation of our overall framework by presenting a set of loop closure results. These results demonstrate that our overall errors in the maps remain small (within 0.91% of the distance traveled for map construction) even when the robot has to traverse over large loops inside a building.

SIFT based automatic number plate recognition

The aim of this paper is on presenting a new and simple, but fast and efficient technique for automatic number plate recognition (ANPR) using SIFT (Scale Invariant Feature Transform) features. The proposed system is used to automatically locate and recognize, as a special case, the Jordanian license plates. In the core of our system, SIFT-based template matching technique is used to locate special marks in the license plate. Upon successful detection of those marks, the license plate is segmented out from the original image and OCR (Optical Character Recognition) is used to recognize the characters or numbers from the plate. Due to the various invariance virtues of SIFT, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, and illumination. Experimental results using real datasets are presented, which show that our system has a good performance.

Modeling and optimization of the lightweight HIGHT block cipher design with FPGA implementation

The growth of low-resource devices has increased rapidly in recent years. Communication in such devices presents two challenges: security and resource limitation. Lightweight ciphers, such as HIGHT cipher, are encryption algorithms targeted for low resource systems. Designing lightweight ciphers in reconfigurable platform e.g., field-programmable gate array provides speedup as well as flexibility. The HIGHT cipher consists of simple operations and provides adequate security level. The objective of this research work is to design, optimize, and model FPGA implementation of the HIGHT cipher. Several optimized designs are presented to minimize the required hardware resources and energy including the scalar and pipeline ones. Our analysis shows that the scalar designs have smaller area and power dissipation, whereas the pipeline designs have higher throughput and lower energy. Because of the fact that obtaining the best performance out of any implemented design mainly requires balancing the design area and energy, our experimental results demonstrate that it is possible to obtain such optimal performance using the pipeline design with two and four rounds per stage as well as with the scalar design with one and eight rounds. Comparing the best implementations of pipeline and scalar designs, the scalar design requires 18% less resources and 10% less power, while the pipeline design has 18 times higher throughput and 60% less energy consumption. Copyright

Solving the robot-world hand-eye(s) calibration problem with iterative methods

Robot-world, hand-eye calibration is the problem of determining the transformation between the robot end-effector and a camera, as well as the transformation between the robot base and the world coordinate system. This relationship has been modeled as

Parameterizations for reducing camera reprojection error for robot-world hand-eye calibration

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An approach-path independent framework for place recognition and mobile robot localization in interior hallways

AX=ZB, where

Analyzing Cyber-Physical Threats on Robotic Platforms

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