• ETRI Journal
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• v.43 no.4
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• pp.617-629
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• 2021

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.98-109
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• 2010

This paper deals with a new method of unmanned aerial vehicle (UAV) recovery when a UAV fails to get a global positioning system (GPS) signal at an unprepared site. The proposed method is based on the simultaneous localization and mapping (SLAM) algorithm. It is a process by which a vehicle can build a map of an unknown environment and simultaneously use this map to determine its position. Extensive research on SLAM algorithms proves that the error in the map reaches a lower limit, which is a function of the error that existed when the first observation was made. For this reason, the proposed method can help an inertial navigation system to prevent its error of divergence with regard to the vehicle position. In other words, it is possible that a UAV can navigate with reasonable positional accuracy in an unknown environment without the aid of GPS. This is the main idea of the present paper. Especially, this paper focuses on path planning that maximizes the discussed ability of a SLAM algorithm. In this work, a SLAM algorithm based on extended Kalman filter is used. For simplicity's sake, a blimp-type of UAV model is discussed and three-dimensional pointed-shape landmarks are considered. Finally, the proposed method is evaluated by a number of simulations.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.353-358
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• 2022

Due to the limited field of view of the pinhole camera, there is a lack of stability and accuracy in camera pose estimation applications such as visual SLAM. Nowadays, multiple-camera setups and large field of cameras are used to solve such issues. However, a multiple-camera system increases the computation complexity of the algorithm. Therefore, in multiple camera-assisted visual simultaneous localization and mapping (vSLAM) the multi-view tracking algorithm is proposed that can be used to balance the budget of the features in tracking and local mapping. The proposed algorithm is based on PanoSLAM architecture with a panoramic camera model. To avoid the scale issue 3D LiDAR is fused with omnidirectional camera setup. The depth is directly estimated from 3D LiDAR and the remaining features are triangulated from pose information. To validate the method, we collected a dataset from the outdoor environment and performed extensive experiments. The accuracy was measured by the absolute trajectory error which shows comparable robustness in various environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.487-493
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• 2012

SLAM(Simultaneous Localization And Mapping) is a technique used by robots and autonomous vehicles to build up a map within an unknown environment and estimate a place of robot. FastSLAM(A Factored Solution to the SLAM) is one of representative method of SLAM, which is based on particle filter and extended Kalman filter. However it is suffered from loss of particle diversity. In this paper, new approach using fitness sharing is proposed to supplement loss of particle diversity, compared and analyzed with existing methods.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.339-346
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• 2006

Increased usage of autonomous underwater vehicle (AUV) has led to the development of alternative navigational methods that do not employ the acoustic beacons and dead reckoning sensors. This paper describes a simultaneous localization and mapping (SLAM) scheme that uses range sonars mounted on a small AUV. The SLAM is one of such alternative navigation methods for measuring the environment that the vehicle is passing through and providing relative position of AUV by processing the data from sonar measurements. A technique for SLAM algorithm which uses several ranging sonars is presented. This technique utilizes an unscented Kalman filter to estimate the locations of the AUV and objects. In order for the algorithm to work efficiently, the nearest neighbor standard filter is introduced as the algorithm of data association in the SLAM for associating the stored targets the sonar returns at each time step. The proposed SLAM algorithm is tested by simulations under various conditions. The results of the simulation show that the proposed SLAM algorithm is capable of estimating the position of the AUV and the object and demonstrates that the algorithm will perform well in various environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.442-448
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• 2011

There are many research results about a self-localization technique of mobile robot. In this paper we present a self-localization technique based on the features of ceiling vision using a fisheye lens. The features obtained by SIFT(Scale Invariant Feature Transform) can be used to be matched between the previous image and the current image and then its optimal function is derived. The fisheye lens causes some distortion on its images naturally. So it must be calibrated by some algorithm. We here propose some methods for calibration of distorted images and design of a geometric fitness model. The proposed method is applied to laboratory and aile environment. We show its feasibility at some indoor environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.12
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• pp.1246-1253
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• 2009

To be high efficient for a navigation of unmanned ground vehicle, it must be able to distinguish between safe and hazardous regions in its immediate environment. We present an advanced method using laser range finder for building global 2D digital maps that include environment information. Laser range finder is used for mapping of obstacles and driving environment in the 2D laser plane. Rotary encoders are used for localization of UGV. The main contributions of this research are the development of an algorithm for global 2D map building and it will turn a UGV navigation based on map matching into a possibility. In this paper, a map building algorithm will be introduced and an assessment of algorithm reliability is judged at an each environment.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.1
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• pp.55-60
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• 2011

The most basic algorithm in SLAM(Simultaneous Localization And Mapping) technique of mobile robots is EKF(Extended Kalman Filter) SLAM. However, it requires prior information of characteristics of the system and the noise model which cannot be estimated in accurate. By this limit, Kalman Filter shows the following behaviors in a highly uncertain environment: becomes too sensitive to internal parameters, mathematical consistency is not kept, or yields a wrong estimation result. In contrast, $H_{\infty}$ filter does not requires a prior information in detail. Thus, based on a idea that $H_{\infty}$ filter based SLAM will be more robust than the EKF-SLAM, we propose a framework of $H_{\infty}$ filter based SLAM and show that suggested algorithm shows slightly better result man me EKF-SLAM in a highly uncertain environment.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.334-338
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• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.