Sirma Yavuz

A comparison of EKF, UKF, FastSLAM2.0, and UKF-based FastSLAM algorithms

This study aims to contribute a comparison of various simultaneous localization and mapping (SLAM) algorithms that have been proposed in literature. The performance of Extended Kalman Filter (EKF) SLAM, Unscented Kalman Filter (UKF) SLAM, EKF-based FastSLAM version 2.0, and UKF-based FastSLAM (uFastSLAM) algorithms are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on Player/Stage environment. The results show that the UKF-based FastSLAM has the best performance in terms of accuracy of localization and mapping. Unlike most of the previous applications of FastSLAM in literature, no waypoints are used in this study.

Security problems for a pseudorandom sequence generator based on the Chen chaotic system

Abstract Recently, a novel pseudorandom number generator scheme based on the Chen chaotic system was proposed. In this study, we analyze the security weaknesses of the proposed generator. By applying a brute force attack on a reduced key space, we show that 66% of the generated pseudorandom number sequences can be revealed. Executable C# code is given for the proposed attack. The computational complexity of this attack is O(n) , where n is the sequence length. Both mathematical proofs and experimental results are presented to support the proposed attack.

Security analysis of an image encryption algorithm based on chaos and DNA encoding

Chaos and cryptography have some common features. Due to the close relationship, researchers try to combine the chaotic system with cryptosystem. Despite the many proposals that use various methods in the design of encryption algorithms, there is a definite need for a mathematically rigorous cryptanalysis of these designs. In this study, we analyze the security weaknesses of an image encryption algorithm based on DNA encoding and chaos. By applying chosen-plaintext attacks, we show that all the secret parameters can be revealed.

An automated domain specific stop word generation method for natural language text classification

We propose an automated method for generating domain specific stop words to improve classification of natural language content. Also we implemented a Bayesian natural language classifier working on web pages, which is based on maximum a posteriori probability estimation of keyword distributions using bag-of-words model to test the generated stop words. We investigated the distribution of stop-word lists generated by our model and compared their contents against a generic stop-word list for English language. We also show that the document coverage rank and topic coverage rank of words belonging to natural language corpora follow Zipfs law, just like the word frequency rank is known to follow.

An occupancy grid based SLAM method

Simultaneous localization and mapping (SLAM) is an active area of research. SLAM algorithms should allow the robot to start its movement from a random position in an unknown environment and to build the map of the area while knowing its own position relative to the map. Thus, at the end of the mapping task robot should be able to return where it has started. Especially in real time applications, using limited sensor data, there are still many problems to be conquered. In this study a probabilistic occupancy grid approach is proposed to build the map of an unknown environment. The method tested both in a simulation environment and on a real robot. Although there are some improvements to be made, the initial results are promising.

Implementation of frontier-based exploration algorithm for an autonomous robot

Exploration is defined as the selection of target points that yield the biggest contribution to a specific gain function at an initially unknown environment. Exploration for autonomous mobile robots is closely related to mapping, navigation, localization and obstacle avoidance. In this study an autonomous frontier-based exploration strategy is implemented. Frontiers are defined as the border points that are calculated throughout the mapping and navigation stage between known and unknown areas. Frontier-based exploration implementation is compatible with the Robot Operating System (ROS). Also in this study, real robot platform is utilized for testing and the effect of different frontier target assignment approaches are comparatively analyzed by means of total path length and thereby total exploration time.

Analysis of chaotic methods for compression and encryption processes in data communication

Data compression and encryption are critical issues for efficiency and security requirements of information transmission. In order to improve the performance and the flexibility of multimedia applications, it is worthwhile to perform compression and encryption in a single process. Recently Hermassi et al. proposed a method for joint compression and encryption using chaotically mutated Huffman trees. The proposed method based on multiple Huffman tables simultaneously performs encryption and compression by a key-controlled swapping of the left and right branches of the Huffman tree. However, security problems were found. In this study describes the security weakness of the proposed method. By applying chosen-plaintext attacks, we show that secret key can be revealed.

Simultaneous localization and mapping using Extended Kalman Filter

In this study, an offline statistical estimation algorithm based on Extended Kalman Filter method is developed to solve the SLAM (Simultaneous Localization and Map Building) problem. For the application, a robot equipped with only simple and cheap sensors is used. Two of the most frequent problems in SLAM algorithms which are known as loop closing and data association are effectively solved by Extended Kalman Filter method.

Efficiency of Dynamic Local Area Strategy for Frontier-Based Exploration in Indoor Environments

Exploration in unknown environments is a fundamental problem for autonomous robotic systems. The most of the existing exploration algorithms are proposed for indoor environments and aim to minimize the overall exploration time and total travelled distance. In this paper, a modified version of frontier-based exploration approach is presented to decrease exploration time and total distance. This approach introduces two more parameters to the Exploration Transform (ET) algorithm, which evaluates all detected frontiers to select next target point. On the other hand, the proposed approach considers locally observed frontiers, which might be changed with the parameter of dynamic distance. The proposed algorithm is individually tested and compared to ET algorithm with five random starting points in three different environments. Experimental results show that the proposed algorithm provides superior performance over conventional ET algorithm.

Obstacle avoidance with Vector Field Histogram algorithm for search and rescue robots

The capability of avoid obstacles is the one of the key issues in autonomous search-and-rescue robots research area. In this study, the avoiding obstacles capability has been provided to the virtula robots in USARSim environment. The aim is finding the minimum movement when robot faces an obstacle in path. For obstacle avoidance we used an real time path planning method which is called Vector Field Histogram (VFH). After experiments we observed that VFH method is successful method for obstacle avoidance. Moreover, the usage of VFH method is highly incresing the amount of the visited places per unit time.