An architecture for multi-robot hector mapping

Balcilar, Muhammet; Uslu, Erkan; Çakmak, Furkan; Altuntas, Nihal; Marangoz, Salih; Amasyalı, Mehmet Fatih; Yavuz, Sırma

doi:10.1109/inista.2016.7571842

Cited by 4 publications

(1 citation statement)

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“…The scan matching uses a Gauss-Newton approach as it solves non-linear least squares problems based on occupancy grid maps [34]. This approach optimizes the alignment of beam endpoints with the current map [35]. Hector SLAM is a part of the linear square optimization-based SLAM [36].…”

Section: Simulation Results With Husky Robotmentioning

confidence: 99%

Simulation of simultaneous localization and mapping using 3D point cloud data

Abdul-Rahman

Razak

Mushin

et al. 2019

IJEECS

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<span>Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology. Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.</span>

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Section: Simulation Results With Husky Robotmentioning

confidence: 99%