• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.341-347
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• 2022

This paper presents a comparison result of two simultaneous localization and mapping (SLAM) algorithms for navigation that have been proposed in literature. The performances of Extended Kalman Filter (EKF) SLAM under Gaussian condition, FastSLAM algorithms using Rao-Blackwellised method for particle filtering are compared in terms of accuracy of state estimations for localization of a robot and mapping of its environment. The algorithms were run using the same type of robot on indoor environment. The results show that the Particle filter based FastSLAM has the better performance in terms of accuracy of localization and mapping. The experimental results are discussed and compared.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.10
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• pp.1174-1180
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• 2011

Not all line or point features capable of being extracted by sonar sensors from cluttered home environments are useful for simultaneous localization and mapping (SLAM) due to their ambiguity because it is difficult to determine the correspondence of line or point features with previously registered feature. Confused line and point features in cluttered environments leads to poor SLAM performance. We introduce a sonar feature structure suitable for a cluttered environment and the extended Kalman filter (EKF)-based SLAM scheme. The reliable line feature is expressed by its end points and engaged togather in EKF SLAM to overcome the geometric limits and maintain the map consistency. Experimental results demonstrate the validity and robustness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.463-468
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• 2010

The mobile robot which accomplishes a work in explored region does not know location information of surroundings. Traditionally, simultaneous localization and mapping(SLAM) algorithms solve the localization and mapping problem in explored regions. Among the several SLAM algorithms, the EKF (Extended Kalman Filter) based SLAM is the scheme most widely used. The EKF is the optimal sensor fusion method which has been used for a long time. The odometeric error caused by an encoder can be compensated by an EKF, which fuses different types of sensor data with weights proportional to the uncertainty of each sensor. In many cases the EKF based SLAM requires artificially installed features, which causes difficulty in actual implementation. Moreover, the computational complexity involved in an EKF increases as the number of features increases. And SLAM is a weak point of long operation time. Therefore, this paper presents a symmetrical model based SLAM algorithm(called M-SLAM).

• 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 Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2094-2102
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• 2016

In this paper, we propose an extended Kalman filter(EKF)-based simultaneous localization and mapping(SLAM) method using laser corner pattern matching for mobile robots. SLAM is one of the most important problems of mobile robot. However, existing method has the disadvantage of increasing the computation time, depending on the number of landmarks. To improve computation time, we produce the corner pattern using classified and detected corner points. After producing the corner patterns, it is estimated that mobile robot's global position by matching them. The estimated position is used as measurement model in the EKF. To evaluated proposed method, we preformed the experiments in the indoor environments. Experimental results of proposed method are shown to maintain an accuracy and decrease the computation time.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.391-399
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• 2012

This paper proposes a particle filter approach for SLAM(Simultaneous Localization and Mapping) of a mobile robot. The SLAM denotes estimation of both the robot location and map while the robot navigates in an unknown environment without map. The proposed method estimates robot location simultaneously with the locations of the ultrasonic beacons which constitute landmarks for navigation. The particle filter method represents the locations of the robot and landmarks in probabilistic manner by the distribution of particles. The method takes care of the uncertainty of the landmarks' location as well as that of the robot motion. Therefore, the locations of the landmarks are updated including uncertainty at every sampling time. Performance of the proposed method is verified through simulation and experiments. The method yields practically useful mapping information even if the range data from the landmarks include random noise. Also, it provides more accurate and robust estimation of the robot location than the usual least squares methods or dead-reckoning method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.149-156
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• 2008

SLAM(Simultaneous Localization and Mapping) system is to find a global position and build a map with sensing data when an unmanned-robot navigates an unknown environment. Two kinds of system were developed. One is used distance measurement sensors such as an ultra sonic and a laser sensor. The other is used stereo vision system. The distance measurement SLAM with sensors has low computing time and low cost, but precision of system can be somewhat worse by measurement error or non-linearity of the sensor In contrast, stereo vision system can accurately measure the 3D space area, but it needs high-end system for complex calculation and it is an expensive tool. In this paper, we implement the SLAM system using a single camera image and a PSD sensors. It detects obstacles from the front PSD sensor and then perceive size and feature of the obstacles by image processing. The probability SLAM was implemented using the data of sensor and image and we verify the performance of the system by real experiment.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.197-205
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• 2014

As computer vision algorithms are developed on a continuous basis, the visual information from vision sensors has been widely used in the context of simultaneous localization and mapping (SLAM), called visual SLAM, which utilizes relative motion information between images. This research addresses a visual SLAM framework for online localization and mapping in an unstructured seabed environment that can be applied to a low-cost unmanned underwater vehicle equipped with a single monocular camera as a major measurement sensor. Typically, an image motion model with a predefined dimensionality can be corrupted by errors due to the violation of the model assumptions, which may lead to performance degradation of the visual SLAM estimation. To deal with the erroneous image motion model, this study employs a local bundle optimization (LBO) scheme when a closed loop is detected. The results of comparison between visual SLAM estimation with LBO and the other case are presented to validate the effectiveness of the proposed methodology.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.687-694
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• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.