Eş zamanlı konum belirleme ve haritalama probleminde yeni bir durum tahmin yöntemi olarak parçacık akış filtresi

Abstract

The Simultaneous Localization and Mapping (SLAM) problem, which emerged in the last quarter of the century, has been adapted for territorial, naval and aerial platforms starting from the year of 2000's and some parametric filter approaches such as Kalman Filter based Extended Kalman Filter and Distributed Kalman Filter, the stateestimation methods including nonparametric methods such as Particle Filter, some high level control aspiring, model or graphics-based and particularly image processing techniques has been used along with it. A strong need for performance analysis of the SLAM problem by classification can be mentioned, as it vary considerably in the platform, vehicle, sensor, and media type such as territorial, naval and aerial platforms. The particle flow filter, which put forward in 2009 for the first time, was particularly attractive due to its advantages such as high accuracy and fast convergence. In this research, a Particle Flow Filter based SLAM structure is given including mathematical bases/background of the filter, analysis, an autonomous ground vehicle and a sensor model, for the first time in the literature. According to the simulation results provided with the performance analysis of estimation under uncertainty tools/algorithms, although it has some computational complexity that may cause real time application concerns, the particle flow filter based SLAM performance is superior than other recursive state estimation approaches emerged before in the literature in terms of accuracy. Especially in the measurement environments with less uncertain sensors, it is preferable because it removes the problem of degeneration which arises in the particle filter structure.

Integration of affine ICP into the precise localization problem of smart-AGVs: Procedures, enhancements and challenges

“…Localization approaches are also a part of SLAM algorithms where localization and mapping are computed simultaneously. Some SLAM studies are given in Balcılar et al (2017), Duymaz et al (2017), Grisetti et al (2007), Kohlbrecher et al (2011) and Luo and Qin (2018). However, SLAM algorithms are especially important for applications such as exploring an unknown environment or obtaining the map of a large environment, and doing SLAM is not necessary for logistic applications in a factory where AGVs move from one point to another on a known map.…”

The Use of LIDAR Technology in Architectural Offices

“…The positioning of the robot in the environment depends on the full map. The correct map works in dependency by providing correct positioning [4]. In the probabilistic framework;…”

“…The parametrical methods, such as Kalman Filter and Extended Kalman Filter, have been used for the last 25 years on the SLAM problem. In addition to parametric approaches, the Particle Filter approach, which was introduced for the first time in 2009, has been particularly advantageous in terms of high accuracy and rapid convergence [4]. Particle Filter, which is not connected to a parametrical method such as Gaussian Distribution, is more accurate and faster than other estimation filters.…”