SURF features for efficient robot localization with omnidirectional images

2007 ◽  
Author(s):  
A. C. Murillo ◽  
J. J. Guerrero ◽  
C. Sagues
Keyword(s):  
Robot Localization ◽  
Surf Features ◽  
Omnidirectional Images

A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images

2017 ◽  
Vol 72 ◽  
pp. 1-17 ◽  
Author(s):  
Leandro B. Marinho ◽  
Jefferson S. Almeida ◽  
João Wellington M. Souza ◽  
Victor Hugo C. Albuquerque ◽  
Pedro P. Rebouças Filho
Keyword(s):  
Mobile Robot ◽  
Robot Localization ◽  
Reject Option ◽  
Topological Maps ◽  
Mobile Robot Localization ◽  
Omnidirectional Images ◽  
Localization Approach

Indoor mobile robot localization in dynamic and cluttered environments using artificial landmarks

2019 ◽  
Vol 36 (2) ◽  
pp. 400-419 ◽  
Author(s):  
Farhad Shamsfakhr ◽  
Bahram Sadeghi Bigham ◽  
Amirreza Mohammadi
Keyword(s):  
Density Function ◽  
Statistical Power ◽  
Accurate Estimation ◽  
Support Vector ◽  
Robot Localization ◽  
Data Set ◽  
Content Type ◽  
Cluttered Environments ◽  
Omnidirectional Images ◽  
Markov Localization

Purpose Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments. Design/methodology/approach The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment. Findings The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima. Originality/value By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.

scholarly journals A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images

2021 ◽  
Vol 11 (16) ◽  
pp. 7521
Author(s):  
Mónica Ballesta ◽  
Luis Payá ◽  
Sergio Cebollada ◽  
Oscar Reinoso ◽  
Francisco Murcia
Keyword(s):  
Mobile Robot ◽  
Indoor Environment ◽  
Dynamic Environments ◽  
Robot Localization ◽  
Retrieval Task ◽  
Considerable Success ◽  
Regression Approach ◽  
Learning Technique ◽  
Mobile Robot Localization ◽  
Omnidirectional Images

Understanding the environment is an essential ability for robots to be autonomous. In this sense, Convolutional Neural Networks (CNNs) can provide holistic descriptors of a scene. These descriptors have proved to be robust in dynamic environments. The aim of this paper is to perform hierarchical localization of a mobile robot in an indoor environment by means of a CNN. Omnidirectional images are used as the input of the CNN. Experiments include a classification study in which the CNN is trained so that the robot is able to find out the room where it is located. Additionally, a transfer learning technique transforms the original CNN into a regression CNN which is able to estimate the coordinates of the position of the robot in a specific room. Regarding classification, the room retrieval task is performed with considerable success. As for the regression stage, when it is performed along with an approach based on splitting rooms, it also provides relatively accurate results.

3-D Scan Matching for Mobile Robot Localization over Rough Terrain

2019 ◽  
Vol 139 (9) ◽  
pp. 1041-1050
Author(s):  
Hiroyuki Nakagomi ◽  
Yoshihiro Fuse ◽  
Hidehiko Hosaka ◽  
Hironaga Miyamoto ◽  
Takashi Nakamura ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Rough Terrain ◽  
Robot Localization ◽  
Scan Matching ◽  
Mobile Robot Localization

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

scholarly journals The Role of Global Appearance of Omnidirectional Images in Relative Distance and Orientation Retrieval

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3327
Author(s):  
Vicente Román ◽  
Luis Payá ◽  
Adrián Peidró ◽  
Mónica Ballesta ◽  
Oscar Reinoso
Keyword(s):  
Mobile Robotics ◽  
A Priori ◽  
Computational Cost ◽  
Relevant Information ◽  
Local Features ◽  
New Family ◽  
Lighting Conditions ◽  
Omnidirectional Images ◽  
Great Development

Over the last few years, mobile robotics has experienced a great development thanks to the wide variety of problems that can be solved with this technology. An autonomous mobile robot must be able to operate in a priori unknown environments, planning its trajectory and navigating to the required target points. With this aim, it is crucial solving the mapping and localization problems with accuracy and acceptable computational cost. The use of omnidirectional vision systems has emerged as a robust choice thanks to the big quantity of information they can extract from the environment. The images must be processed to obtain relevant information that permits solving robustly the mapping and localization problems. The classical frameworks to address this problem are based on the extraction, description and tracking of local features or landmarks. However, more recently, a new family of methods has emerged as a robust alternative in mobile robotics. It consists of describing each image as a whole, what leads to conceptually simpler algorithms. While methods based on local features have been extensively studied and compared in the literature, those based on global appearance still merit a deep study to uncover their performance. In this work, a comparative evaluation of six global-appearance description techniques in localization tasks is carried out, both in terms of accuracy and computational cost. Some sets of images captured in a real environment are used with this aim, including some typical phenomena such as changes in lighting conditions, visual aliasing, partial occlusions and noise.

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