Data Association Analysis in Simultaneous Localization and Mapping Problem

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Hamzah Ahmad ◽  
◽  
Nur Aqilah Othman ◽  
Mohd Mawardi Saari ◽  
Mohd Syakirin Ramli ◽  
...  
Keyword(s):  
Association Analysis ◽  
Data Association ◽  
Simultaneous Localization And Mapping ◽  
Mapping Problem ◽  
Localization And Mapping

A Robust Solution to the Stereo-Vision-Based Simultaneous Localization and Mapping Problem with Steady and Moving Landmarks

2011 ◽  
Vol 25 (6-7) ◽  
pp. 765-788 ◽  
Author(s):  
Pubudu N. Pathirana ◽  
Andrey V. Savkinb ◽  
Samitha W. Ekanayake ◽  
Nicholas J. Bauer
Keyword(s):  
Stereo Vision ◽  
Simultaneous Localization And Mapping ◽  
Robust Solution ◽  
Mapping Problem ◽  
Localization And Mapping

scholarly journals Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots

2014 ◽  
Vol 14 (3) ◽  
pp. 86-95 ◽  
Author(s):  
Yingmin Yi ◽  
Ying Huang
Keyword(s):  
Correlation Function ◽  
Graph Theory ◽  
Sequence Data ◽  
Data Association ◽  
Simultaneous Localization And Mapping ◽  
Geometric Information ◽  
Maximum Correlation ◽  
Noise Uncertainty ◽  
Localization And Mapping

Abstract The paper proposes landmark sequence data association for Simultaneous Localization and Mapping (SLAM) for data association problem under conditions of noise uncertainty increase. According to the space geometric information of the environment landmarks, the information correlations between the landmarks are constructed based on the graph theory. By observing the variations of the innovation covariance using the landmarks of the adjacent two steps, the problem is converted to solve the landmark TSP problem and the maximum correlation function of the landmark sequences, thus the data association of the observation landmarks is established. Finally, the experiments prove that our approach ensures the consistency of SLAM under conditions of noise uncertainty increase.

scholarly journals Simultaneous localization and mapping: A feature-based probabilistic approach

2009 ◽  
Vol 19 (4) ◽  
pp. 575-588 ◽  
Author(s):  
Piotr Skrzypczyński
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Data Association ◽  
Probabilistic Approach ◽  
Simultaneous Localization And Mapping ◽  
Probabilistic Formulation ◽  
Recent Advances ◽  
Localization And Mapping ◽  
Feature Based ◽  
Points Of View

Simultaneous localization and mapping: A feature-based probabilistic approachThis article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.

Simultaneous localization and mapping navigation of unmanned ground vehicle based on second-order smooth variable structure filter with improved technique to combat fading for advanced wireless communications

2020 ◽  
pp. 095965182096572
Author(s):  
Abdelkader Mosbah ◽  
Fethi Demim ◽  
Ali Mansoul ◽  
Mustapha Benssalah ◽  
Abdelkrim Nemra
Keyword(s):  
Wireless Communications ◽  
Wireless Communication ◽  
Mobile Robot ◽  
Communication Systems ◽  
Simultaneous Localization And Mapping ◽  
Variable Structure ◽  
Second Order ◽  
Mapping Problem ◽  
Localization And Mapping ◽  
Smooth Variable Structure Filter

Simultaneous localization and mapping is very essential for autonomous navigation when the mobile robot is navigating in unknown environment without a global positioning system. Various techniques to solve the simultaneous localization and mapping problem have been extensively studied using the combination of low-cost sensors. Most of the work in mobile robotics still consists of finding solutions to problems in data exchange between mobile robot and communication control station, which is a challenging task. In fact, communication systems impose severe constraints in terms of channel capacity and transmission quality, because the transmission channel in communication systems is undergoing at the different physical phenomena like scattering, diffusion and diffraction, which occur interference and multiple path effects in wireless communications, while keeping these effects levels low. This article describes a simultaneous localization and mapping problem based on second-order smooth variable structure filter embedded in mobile robot equipped with a sensor for data wireless collection. The inclusion of the control in environments outside the mobile robot field of view can make the wireless communication simultaneous localization and mapping process much more difficult to find a solution under realistic conditions. In order to solve the simultaneous localization and mapping issue and to mitigate the fading phenomena, which affect the quality of service in advanced wireless communication systems, we use a new approach to combat the fading effect without requiring any statistical knowledge of the propagation channel parameters. Several experiments have been done in real-world applications, and good performances were obtained using a second-order smooth variable structure filter–simultaneous localization and mapping algorithm–based wireless communication.

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