Abstract
The navigation is a substantial issue in the field of robotics. Simultaneous Localization and Mapping (SLAM) is a principle for many autonomous navigation applications, particularly in the Global Navigation Satellite System (GNSS) denied environments. Many SLAM methods made substantial contributions to improve its accuracy, cost, and efficiency. Still, it is a considerable challenge to manage robust SLAM, and there exist several attempts to find better estimation algorithms for it. In this research, we proposed a novel Bayesian filtering based Airborne SLAM structure for the first time in the literature. We also presented the mathematical background of the algorithm, and the SLAM model of an autonomous aerial vehicle. Simulation results emphasize that the new Airborne SLAM performance with the exact flow of particles using for recursive state estimations superior to other approaches emerged before, in terms of accuracy and speed of convergence. Nevertheless, its computational complexity may cause real-time application concerns, particularly in high-dimensional state spaces. However, in Airborne SLAM, it can be preferred in the measurement environments that use low uncertainty sensors because it gives more successful results by eliminating the problem of degeneration seen in the particle filter structure.
Highlights
Autonomous systems have quite an importance for decision-makers and operational agents due to a large variety of its applications such as reconnaissance and surveillance in the military and civilian areas and their other extensive usage
This study looks for a better alternative of recursive state estimations under uncertainty for the problem of Airborne Simultaneous Localization and Mapping (A-SLAM)
The trajectory of an unmanned aerial system operating within the same PFF based A-SLAM scenario is depicted in Fig 7 in order to see Particle Flow Filter based A-SLAM algorithm performance during research
Summary
Autonomous systems have quite an importance for decision-makers and operational agents due to a large variety of its applications such as reconnaissance and surveillance in the military and civilian areas and their other extensive usage. This study looks for a better alternative of recursive state estimations under uncertainty for the problem of Airborne Simultaneous Localization and Mapping (A-SLAM) Despite their success points, the prevalent systems ( Extended Kalman and Particle Filters) used in A-SLAM could produce a solution neither for drift problems that exhibit non-gaussian structure nor particle degeneracy problems that inhibit exact convergence. The prevalent systems ( Extended Kalman and Particle Filters) used in A-SLAM could produce a solution neither for drift problems that exhibit non-gaussian structure nor particle degeneracy problems that inhibit exact convergence Since these problems crucial for autonomous navigation, we propose to use the ’Exact Flow of Particles’ approach for A-SLAM state estimations and hope to solve them and find out better results. The following parts will include related works, the exact flow of particles‘theory and its A-SLAM application, simulations, results, discussion, and conclusion, respectively
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
