Exact flow of particles using for state estimations in unmanned aerial systems` navigation

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.

A multi-stage localization framework for accurate and precise docking of autonomous mobile robots (AMRs)

“…The results demonstrated improved performance compared to the traditional Extended Kalman Filter. Similarly, many more techniques are available in the literature, including rigid body localization using wireless sensor networks [11,12], WiFi-based localization architecture [13,14], vision-based approaches using different types of cameras [15][16][17], and particle filter-based approaches such as Adaptive Monte Carlo Localization (MCL) [18], Self-Adaptive MCL (SA-MCL) [19], and particle flow filtering architecture [20]. It is also possible to improve their performance with suggested arrangements [21].…”

LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information

“…Research on loop closure detection is plentiful [ 6 – 9 ], but the open-source available work is less known [ 10 ]. For works using the graph SLAM [ 11 ], LeGO-LOAM [ 3 ], and LIO-SAM [ 12 ], or filter SLAM [ 13 ], the loop closure part still uses traditional Euclidean distance. The scan-context [ 9 ] is also known as an available loop closure work.…”