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

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

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.39 no.4
• /
• pp.362-369
• /
• 2011

In this paper, vSLAM aided integrated navigation system is implemented and performance analysis of the system is completed via flight test. The system can suppress divergence of position error of INS only system by updating vSLAM correction information when temporary GPS signal outage occurs in bad radio condition. In the flight test, integrated hardware containing GPS, IMU and camera is loaded under RC electric helicopter. Performance of the integrated navigation system is verified by comparing estimated position of INS/vSLAM system with that of INS only system.

• Journal of IKEEE
• /
• v.19 no.4
• /
• pp.557-565
• /
• 2015

In this paper, a method for estimating the location of a quadcopter is proposed by applying an EKF-SLAM algorithm to its flight control, to autonomously control the flight of an unmanned quadcopter. The usefulness of this method is validated through simulations. For autonomously flying the unmanned quadcopter, an algorithm is required to estimate its accurate location, and various approaches exist for this. Among them, SLAM, which has seldom been applied to the quadcopter flight control, was applied in this study to simulate a system that estimates flight trajectories of the quadcopter.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.4
• /
• pp.637-644
• /
• 2019

This paper describes an implementation of a graph-based simultaneous localization and mapping(SLAM) method called the General Graph Optimization. The General Graph Optimization formulates the SLAM problem using nodes and edges. The nodes represent the location and attitude of a robot in time sequence, and the edge between the nodes depict the constraint between the nodes. The constraints are imposed by sensor measurements. The General Graph Optimization solves the problem by optimizing the performance index determined by the constraints. The implementation is verified using the measurement data sets which are open for test of various SLAM methods.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1998.06c
• /
• pp.227.1-230
• /
• 1998

본 연구에서는 기존의 SLAM에서는 불가능한 진폭과 위상 정보를 동시에 검출할 수 있는 쿼드러춰 검출기를 설계 제작하여 SLAM을 구성하고 진폭과 위상영상을 복원하여 기존의 SALM 영상과 비교분석하였다. 실험을 위하여 동작주파수가 10MHz인 쿼드러춰 검출기를 제작하여 SLAM시스템을 구성하고 시편으로는 다른 패턴을 갖는 두 개의 층으로 이루어진 평면구조물을 알루미늄으로 가공하여 실험하였다. 실험결과 다층구조물에 대한 진폭과 위상 영상을 복원할 수 있었고 기존의 SLAM 영상과 비교하여 양호한 분해능과 콘트라스트를 나타냈으며 특히 기존의 방법으로는 얻을 수 없었던 위상영상을 얻을 수 있었다.

• The Journal of Korea Robotics Society
• /
• v.4 no.2
• /
• pp.97-104
• /
• 2009

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, due to the usage of the accurate sensors, distinct particles which compensate one another are attenuated as the RBPF-SLAM continues. To avoid this particle depletion, we propose the strategic games to assign the particle's payoff which replaces the importance weight in the current RBPF-SLAM framework. From simulation works, we show that RBPF-SLAM with the strategic games is inconsistent in the pessimistic way, which is different from the existing optimistic RBPF-SLAM. In addition, first, the estimation errors with applying the strategic games are much less than those of the standard RBPF-SLAM, and second, the particle depletion is alleviated.

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

• The Journal of Korea Robotics Society
• /
• v.4 no.4
• /
• pp.305-311
• /
• 2009

FastSLAM is a factored solution to SLAM problem using a Rao-Blackwellized particle filter. In this paper, we propose a practical FastSLAM implementation method using an infrared camera for indoor environments. The infrared camera is equipped on a Pioneer3 robot and looks upward direction to the ceiling which has infrared tags with the same height. The infrared tags are detected with theinfrared camera as measurements, and the Nearest Neighbor method is used to solve the unknown data association problem. The global map is successfully built and the robot pose is predicted in real time by the FastSLAM2.0 algorithm. The experiment result shows the accuracy and robustness of the proposed method in practical indoor environment.

• The Journal of Korea Robotics Society
• /
• v.15 no.3
• /
• pp.205-211
• /
• 2020

In this paper, we propose a modified ORB-SLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization And Mapping) for precise indoor navigation of a mobile robot. The exact posture and position estimation by the ORB-SLAM is not possible all the times for the indoor navigation of a mobile robot when there are not enough features in the environment. To overcome this shortcoming, additional IMU (Inertial Measurement Unit) and encoder sensors were installed and utilized to calibrate the ORB-SLAM. By fusing the global information acquired by the SLAM and the dynamic local location information of the IMU and the encoder sensors, the mobile robot can be obtained the precise navigation information in the indoor environment with few feature points. The superiority of the modified ORB-SLAM was verified to compared with the conventional algorithm by the real experiments of a mobile robot navigation in a corridor environment.