scholarly journals Fuzzy Data Association of Aerial Robot Monocular SLAM

2016 ◽  
Author(s):  
Yin-Tien Wang ◽  
Ting-Wei Chen
Keyword(s):  
Data Association ◽  
Fuzzy Data ◽  
Monocular Slam ◽  
Aerial Robot

A new fuzzy data association based on attribute features

2014 ◽  
Author(s):  
Xi-chen Niu ◽  
Hong-bin Jin ◽  
Jia-jun Xiong ◽  
Li-cai Wang
Keyword(s):  
Data Association ◽  
Fuzzy Data

scholarly journals Validation of Data Association for Monocular SLAM

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Edmundo Guerra ◽  
Rodrigo Munguia ◽  
Yolanda Bolea ◽  
Antoni Grau
Keyword(s):  
Data Fusion ◽  
Data Association ◽  
Sensory Systems ◽  
Improve Performance ◽  
Compatibility Test ◽  
Monocular Slam ◽  
Slam Algorithm ◽  
Key Aspects

Simultaneous Mapping and Localization (SLAM) is a multidisciplinary problem with ramifications within several fields. One of the key aspects for its popularity and success is the data fusion produced by SLAM techniques, providing strong and robust sensory systems even with simple devices, such as webcams in Monocular SLAM. This work studies a novel batch validation algorithm, the highest order hypothesis compatibility test (HOHCT), against one of the most popular approaches, the JCCB. The HOHCT approach has been developed as a way to improve performance of the delayed inverse-depth initialization monocular SLAM, a previously developed monocular SLAM algorithm based on parallax estimation. Both HOHCT and JCCB are extensively tested and compared within a delayed inverse-depth initialization monocular SLAM framework, showing the strengths and costs of this proposal.

scholarly journals A Novel Fuzzy Data Association Approach for Visual Multi-object Tracking

2017 ◽  
Vol 12 ◽  
pp. 05004
Author(s):  
Liang-Qun Li ◽  
En-Qun Li ◽  
Wen-Ming He
Keyword(s):  
Object Tracking ◽  
Data Association ◽  
Fuzzy Data

Batch Gating for Data Association in Monocular SLAM

2013 ◽  
Vol 319 ◽  
pp. 295-301
Author(s):  
Edmundo Guerra ◽  
Rodrigo Munguia ◽  
Yolanda Bolea ◽  
Antoni Grau
Keyword(s):  
Search Algorithm ◽  
Data Association ◽  
Real Data ◽  
Single Camera ◽  
Data Validation ◽  
Compatibility Test ◽  
Monocular Slam ◽  
Validation Technique

This work describes the development and implementation of a single-camera SLAM system, introducing a novel data validation algorithm. A 6-DOF monocular SLAM method developed is based on the Delayed Inverse-Depth (DI-D) Feature Initialization, with the addition of a new data association batch validation technique, the Highest Order Hypothesis Compatibility Test, HOHCT. The DI-D initializes new features in the system defining single hypothesis for the initial depth of features by stochastic triangulation. The HOHCT is based on evaluation of statistically compatible hypotheses, and search algorithm designed to exploit the Delayed Inverse-Depth technique characteristics. Experiments with real data are presented in order to validate the performance of the system.

Directional Fuzzy Data Association Filter

2012 ◽  
Vol 7 (10) ◽  
Author(s):  
Pengfei Li ◽  
Jingxiong Huang ◽  
Lixin Ye ◽  
Yi Wang ◽  
Zhijun Li ◽  
...  
Keyword(s):  
Data Association ◽  
Fuzzy Data

Modified Maximum Entropy Fuzzy Data Association Filter

2010 ◽  
Vol 132 (2) ◽  
Author(s):  
Abdolreza Dehghani Tafti ◽  
Nasser Sadati
Keyword(s):  
Target Tracking ◽  
Maximum Entropy ◽  
Fuzzy Clustering ◽  
Data Association ◽  
Computational Cost ◽  
Fuzzy Data ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Cluttered Environment ◽  
Fuzzy Clustering Method

The problem of fuzzy data association for target tracking in a cluttered environment is discussed in this paper. In data association filters based on fuzzy clustering, the association probabilities of tracking filters are reconstructed by utilizing the fuzzy membership degree of the measurement belonging to the target. Clearly in these filters, the fuzzy clustering method has an important role; better approach causes better precision in target tracking. Recently, by using the information theory, the maximum entropy fuzzy data association filter (MEF-DAF), as a fast and efficient algorithm, is introduced in literature. In this paper, by modification of a fuzzy clustering objective function, which is prepared for using in target tracking, a modified maximum entropy fuzzy data association filter (MMEF-DAF) is proposed. The MMEF-DAF has a better performance in case of single and multiple target tracking than MEF-DAF, and the other known algorithms such as probabilistic data association filter and the hybrid fuzzy data association filter. Using Monte Carlo simulations, the superiority of the proposed algorithm in comparison with the previous ones is demonstrated. Simply, less computational cost and suitability for real-time applications are the main advantages of the proposed algorithm.

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