In this paper, we present an enhanced loop closure method* based on image-to-image matching relies on quantized local Zernike moments. In contradistinction to the previous methods, our approach uses additional depth
information to extract Zernike moments in local manner. These moments are used to represent holistic shape
information inside the image. The moments in complex space that are extracted from both grayscale and depth
images are coarsely quantized. In order to find out the similarity between two locations, nearest neighbour (NN)
classification algorithm is performed. Exemplary results and the practical implementation case of the method
are also given with the data gathered on the testbed using a Kinect. The method is evaluated in three different
datasets of different lighting conditions. Additional depth information with the actual image increases the detection rate especially in dark environments. The results are referred as a successful, high-fidelity online method
for visual place recognition as well as to close navigation loops, which is a crucial information for the well known
simultaneously localization and mapping (SLAM) problem. This technique is also practically applicable because
of its low computational complexity, and performing capability in real-time with high loop closing accuracy.
Simultaneous Localization and Mapping (SLAM) is a good choice for UAV navigation when both UAV’s position and region map are not known. Due to nonlinearity of kinematic equations of a UAV, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are employed. In this study, EKF and UKF based A-SLAM concepts are discussed in details by presenting the formulations and simulation results. The UAV kinematic model and the state-observation models for EKF and UKF based A-SLAM methods are developed to analyze the filters' consistencies. Analysis during landmark observation exhibits an inconsistency in the form of a jagged UAV trajectory. It has been found that unobservable subspaces and the Jacobien matrices used for linearization are two major sources of the inconsistencies observed. UKF performs better in terms of filter consistency since it does not require the Jacobien matrix linearization.
Simultaneous Localization and Mapping (SLAM) is a fundamental problem of the autonomous systems in GPS (Global
Navigation System) denied environments. The traditional probabilistic SLAM methods uses point features as landmarks
and hold all the feature positions in their state vector in addition to the robot pose. The bottleneck of the point-feature
based SLAM methods is the data association problem, which are mostly based on a statistical measure. The data
association performance is very critical for a robust SLAM method since all the filtering strategies are applied after a
known correspondence. For point-features, two different but very close landmarks in the same scene might be confused
while giving the correspondence decision when their positions and error covariance matrix are solely taking into account.
Instead of using the point features, planar features can be considered as an alternative landmark model in the SLAM
problem to be able to provide a more consistent data association. Planes contain rich information for the solution of the
data association problem and can be distinguished easily with respect to point features. In addition, planar maps are very
compact since an environment has only very limited number of planar structures. The planar features does not have to be
large structures like building wall or roofs; the small plane segments can also be used as landmarks like billboards, traffic
posts and some part of the bridges in urban areas. In this paper, a probabilistic plane-feature extraction method from 3DLiDAR
data and the data association based on the extracted semantic information of the planar features is introduced.
The experimental results show that the semantic data association provides very satisfactory result in outdoor 6D-SLAM.
This paper introduces a novel image description technique that aims at appearance based loop closure detection
for mobile robotics applications. This technique relies on the local evaluation of the Zernike Moments. Binary
patterns, which are referred to as Local Zernike Moment (LZM) patterns, are extracted from images, and these
binary patterns are coded using histograms. Each image is represented with a set of histograms, and loop closure
is achieved by simply comparing the most recent image with the images in the past trajectory. The technique
has been tested on the New College dataset, and as far as we know, it outperforms the other methods in terms
of computation efficiency and loop closure precision.
This paper presents an approach to detect visually salient patches in order to use them for visual place recognition.
We formulate the saliency detection problem as an optimization problem, and define an energy function which
describes the distinctiveness of a given image patch. We employ a Branch & Bound based search technique to
efficiently find the global optimum of the energy function. Moreover, we use integral images to further increase
the efficiency of the approach. The proposed saliency detection technique is able to detect patches which are
suitable to be used as visual landmarks, and it performs with very high efficiency.
Simultaneous Localization and Mapping (SLAM) using for the mobile robot navigation has two main problems. First
problem is the computational complexity due to the growing state vector with the added landmark in the environment.
Second problem is data association which matches the observations and landmarks in the state vector. In this study, we
compare Extended Kalman Filter (EKF) based SLAM which is well-developed and well-known algorithm, and Compressed
Extended Kalman Filter (CEKF) based SLAM developed for decreasing of the computational complexity of the EKF based
SLAM. We write two simulation program to investigate these techniques. Firts program is written for the comparison of EKF
and CEKF based SLAM according to the computational complexity and covariance matrix error with the different numbers
of landmarks. In the second program, EKF and CEKF based SLAM simulations are presented. For this simulation differential
drive vehicle that moves in a 10m square trajectory and LMS 200 2-D laser range finder are modelled and landmarks are
randomly scattered in that 10m square environment.
In this study, behavior generation and self-learning paradigms are investigated for the real-time
applications of multi-goal mobile robot tasks. The method is capable to generate new behaviors and
it combines them in order to achieve multi goal tasks. The proposed method is composed from
three layers: Behavior Generating Module, Coordination Level and Emotion -Motivation Level.
Last two levels use Hidden Markov models to manage dynamical structure of behaviors. The
kinematics and dynamic model of the mobile robot with non-holonomic constraints are considered
in the behavior based control architecture. The proposed method is tested on a four-wheel driven
and four-wheel steered mobile robot with constraints in simulation environment and results are
obtained successfully.
This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model
developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of
architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to
emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also
motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.
This study implements a digital map building method for a mobile robot operating in an environment with obstacles by fusing sensor data. Required information for a map designing is supplied by fusion of different sensor data using sequential principal component method. We discus mathematical and experimental issues of the method. Application of the method for grid based map building is introduced and suitability in mobile robot navigation is demonstrated. Experimental studies are implemented on Nomad200 mobile robot successfully.
This paper represents a novel smooth trajectory planning algorithm which uses the natural behavior model of a mobile robot (MR). The shortest path in the free configuration space is obtained by using the visibility graph method. It is modified according to dynamic constraints which are implicitly included in the natural behavior of the mobile robot. The modified path becomes a smooth, easily tractable time-optimal trajectory. For every points of it, translating/steering velocities and accelerations and reaching times are known. It is applicable to the real-time dynamic configuration spaces, because of simplicity and low computational time.
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