scholarly journals Three Landmark Optimization Strategies for Mobile Robot Visual Homing

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3180 ◽  
Author(s):  
Xun Ji ◽  
Qidan Zhu ◽  
Junda Ma ◽  
Peng Lu ◽  
Tianhao Yan
Keyword(s):  
Mobile Robot ◽  
Target Location ◽  
Mobile Robot Navigation ◽  
Scale Invariant ◽  
Visual Homing ◽  
Vision Sensors ◽  
Homing Performance ◽  
Sift Features ◽  
Scale Invariant Feature ◽  
Input Form

Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies.

scholarly journals TOWARDS CONTINUOUS CONTROL FOR MOBILE ROBOT NAVIGATION: A REINFORCEMENT LEARNING AND SLAM BASED APPROACH

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 857-863 ◽  
Author(s):  
K. A. A. Mustafa ◽  
N. Botteghi ◽  
B. Sirmacek ◽  
M. Poel ◽  
S. Stramigioli
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robot ◽  
Target Location ◽  
Mobile Robot Navigation ◽  
Continuous Control ◽  
Local Optima ◽  
Reward Function ◽  
Angular Velocities ◽  
Planning Algorithm ◽  
Particle Filter Algorithm

<p><strong>Abstract.</strong> We introduce a new autonomous path planning algorithm for mobile robots for reaching target locations in an unknown environment where the robot relies on its on-board sensors. In particular, we describe the design and evaluation of a deep reinforcement learning motion planner with continuous linear and angular velocities to navigate to a desired target location based on deep deterministic policy gradient (DDPG). Additionally, the algorithm is enhanced by making use of the available knowledge of the environment provided by a grid-based SLAM with Rao-Blackwellized particle filter algorithm in order to shape the reward function in an attempt to improve the convergence rate, escape local optima and reduce the number of collisions with the obstacles. A comparison is made between a reward function shaped based on the map provided by the SLAM algorithm and a reward function when no knowledge of the map is available. Results show that the required learning time has been decreased in terms of number of episodes required to converge, which is 560 episodes compared to 1450 episodes in the standard RL algorithm, after adopting the proposed approach and the number of obstacle collision is reduced as well with a success ratio of 83% compared to 56% in the standard RL algorithm. The results are validated in a simulated experiment on a skid-steering mobile robot.</p>

scholarly journals THE LOW BACKSCATTERING OBJECTS CLASSIFICATION IN POLSAR IMAGE BASED ON BAG OF WORDS MODEL USING SUPPORT VECTOR MACHINE

2018 ◽  
Vol XLII-3 ◽  
pp. 2089-2091
Author(s):  
L. Yang ◽  
L. Shi ◽  
P. Li ◽  
J. Yang ◽  
L. Zhao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Bare Soil ◽  
Radar Signal ◽  
Support Vector ◽  
Data Sets ◽  
Bag Of Words ◽  
Scale Invariant ◽  
Polarimetric Synthetic Aperture Radar ◽  
Sift Features ◽  
Scale Invariant Feature

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

Merging grid maps of different resolutions by scaling registration

Robotica ◽  
2015 ◽  
Vol 34 (11) ◽  
pp. 2516-2531 ◽  
Author(s):  
Liang Ma ◽  
Jihua Zhu ◽  
Li Zhu ◽  
Shaoyi Du ◽  
Jingru Cui
Keyword(s):  
Objective Function ◽  
Superior Performance ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Consistent Feature ◽  
Initial Transformation ◽  
Random Sample Consensus ◽  
Sift Features ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

SUMMARYThis paper considers the problem of merging grid maps that have different resolutions. Because the goal of map merging is to find the optimal transformation between two partially overlapping grid maps, it can be viewed as a special image registration issue. To address this special issue, the solution considers the non-common areas and designs an objective function based on the trimmed mean-square error (MSE). The trimmed and scaling iterative closest point (TsICP) algorithm is then proposed to solve this well-designed objective function. As the TsICP algorithm can be proven to be locally convergent in theory, a good initial transformation should be provided. Accordingly, scale-invariant feature transform (SIFT) features are extracted for the maps to be potentially merged, and the random sample consensus (RANSAC) algorithm is employed to find the geometrically consistent feature matches that are used to estimate the initial transformation for the TsICP algorithm. In addition, this paper presents the rules for the fusion of the grid maps based on the estimated transformation. Experimental results carried out with publicly available datasets illustrate the superior performance of this approach at merging grid maps with respect to robustness and accuracy.

Implementation of Image Matching Algorithm Based on SIFT Features

2014 ◽  
Vol 602-605 ◽  
pp. 3181-3184 ◽  
Author(s):  
Mu Yi Yin ◽  
Fei Guan ◽  
Peng Ding ◽  
Zhong Feng Liu
Keyword(s):  
Image Matching ◽  
Implementation Process ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Source Codes ◽  
Invariant Feature ◽  
Viewpoint Change ◽  
Sift Features ◽  
Scale Invariant Feature

With the aim to solve the implement problem in scale invariant feature transform (SIFT) algorithm, the theory and the implementation process was analyzed in detail. The characteristics of the SIFT method were analyzed by theory, combined with the explanation of the Rob Hess SIFT source codes. The effect of the SIFT method was validated by matching two different real images. The matching result shows that the features extracted by SIFT method have excellent adaptive and accurate characteristics to image scale, viewpoint change, which are useful for the fields of image recognition, image reconstruction, etc.

scholarly journals Hybrid Image-Retrieval Method for Image-Splicing Validation

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 83 ◽  
Author(s):  
Nam Pham ◽  
Jong-Weon Lee ◽  
Goo-Rak Kwon ◽  
Chun-Su Park
Keyword(s):  
Image Retrieval ◽  
Zernike Moment ◽  
Invariant Property ◽  
Image Splicing ◽  
Scale Invariant ◽  
Retrieval Method ◽  
One Step ◽  
Sift Features ◽  
Scaling Invariant ◽  
Scale Invariant Feature

Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

Monocular Vision Based Mobile Robot 3D Map Building

2010 ◽  
Vol 43 ◽  
pp. 49-52
Author(s):  
Mao Hai Li ◽  
Li Ning Sun
Keyword(s):  
Mobile Robot ◽  
Feature Matching ◽  
Large Error ◽  
Monocular Vision ◽  
Superior Performance ◽  
Map Building ◽  
Scale Invariant ◽  
Sift Feature ◽  
Search Approach ◽  
Scale Invariant Feature

A robust dense 3D feature map is built only with monocular vision and odometry. Monocular vision mounted on the robot front-end tracks the 3D natural landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature matching pairs. SIFT features are highly distinctive and invariant to image scaling, rotation, and change in 3D viewpoints. A fast SIFT feature matching algorithm is implemented with the KD-Tree based nearest search approach in the time cost of O(log2N), and matches with large error are eliminated by epipolar line restriction. A map building algorithm based on 3D spatial SIFT landmarks is designed and implemented. Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.

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