• The Journal of Korea Robotics Society
• /
• v.7 no.2
• /
• pp.120-128
• /
• 2012

The loop closure problem is one of the most challenging issues in the vision-based simultaneous localization and mapping community. It requires the robot to recognize a previously visited place from current camera measurements. While the loop closure often relies on visual bag-of-words based on point features in the previous works, however, in this paper we propose a line-based method to solve the loop closure in the corridor environments. We used both the floor line and the anchored vanishing point as the loop closing feature, and a two-step loop closure algorithm was devised to detect a known place and perform the global pose correction. We propose an anchored vanishing point as a novel loop closure feature, as it includes position information and represents the vanishing points in bi-direction. In our system, the accumulated heading error is reduced using an observation of a previously registered anchored vanishing points firstly, and the observation of known floor lines allows for further pose correction. Experimental results show that our method is very efficient in a structured indoor environment as a suitable loop closure solution.

• Proceedings of the Korean Geotechical Society Conference
• /
• 2006.03a
• /
• pp.652-661
• /
• 2006

The cut slopes in Jinju area constitute the Cretaceous Sedimentary rock which is one of the most poor ground conditions. The geological rocks of the cut slopes are correlated with Jinju Formation. Most of the rocks consist of Black Shale layer, but the lower parts consist of Alkorsic White Sandstone. So, It is characteristic of the differential weathering due to the difference of rock species. Moreover, vertical joints which concentrate on the released rock and weak rock fragments are accompanied with minor faults. We make out face mapping about each slopes through the detailed field-study and deduce RMR and SMR from the field data. The strength properties of rocks were obtained from references, indoor tests, and Back Analysis method. And, choosing properties were used in the stability analysis as stereographic projection and limit equilibrium analysis and we establish the countermeasures for the cut slopes.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.10
• /
• pp.936-943
• /
• 2015

This paper presents arc/line segments-based Simultaneous Localization and Mapping (SLAM) by updating accumulated laser sensor data with a mobile robot moving in an unknown environment. For each scan, the sensor data in the set are stored by a small constant number of parameters that can recover the necessary information contained in the raw data of the group. The arc and line segments are then extracted according to different limit values, but based on the same parameters. If two segments, whether they are homogenous features or not, from two scans are matched successfully, the new segment is extracted from the union set with combined data information obtained by means of summing the equivalent parameters of these two sets, not combining the features directly. The covariance matrixes of the segments are also updated and calculated synchronously employing the same parameters. The experiment results obtained in an irregular indoor environment show the good performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.5
• /
• pp.2299-2318
• /
• 2019

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.6
• /
• pp.13-24
• /
• 2008

The multi robot operation technique has emerged as one of the most important research subjects that focus on minimizing redundancy in space exploration and maximizing the efficiency of operation. For an efficient operation of the multi robot systems, the movement of each Single robot in the multi robot systems should be properly observed and controlled. This paper suggests Multi Robot Management System to minimize redundancy in space exploration by assigning exploration space to each robot efficiently to take advantage of the RFID. Also, this paper has suggested fault tolerance technique that detects disable Single robot and substitute it by activated Single robot in order to ensure overall exploration and improve efficiency of exploration. Proposed system overcomes previous fault that it is difficult for central server to detect exact position of robot by using RFID system and Home Robot. Designated Home robot manages each Single robot efficiently and assigns the best suited space to Single robot by using RFID Tag Information. Proposed multi robot management system uses RFID for space assignment, Localization and Mapping efficiently and not only maximizes the efficiency of operation, but also ensures reliability by supporting fault-tolerance, compared with Single robot system. Also, through simulation, this paper proves efficiency of spending time and redundancy rates between multi robot management applied by proposed system and not applied by proposed system.

• Economic and Environmental Geology
• /
• v.43 no.6
• /
• pp.615-623
• /
• 2010

Seismic refraction survey is a geophysical method that delineates subsurface velocity structure using direct wave and critically refracted wave. The generalized reciprocal method(GRM) is an inversion technique which uses travel-time data from several forward and reverse shots and which can provide the geometry of irregular inclined refractors and structures underlain by hidden layer such as low velocity zone and thin layer. In this study, a simple Excel-GRM routine was tested for fast mapping of the interface between weathering layer and bedrock during the survey, with employing a pair of forward and reverse shots. This routine was proved to control the maximum dip of approximately $30^{\circ}C$ and maximum velocity contrast of 0.6, based on the panel tests in terms of dipping angle and velocity contrast for the two-layer inclined models. In contrast with conventional operation of five to seven shots with sufficient offset distance and indoor data analysis thereafter, this routine was performed in the field shortly after data acquisition. Depth to the bedrock provided by Excel-GRM, during the field survey for Cheongju granite area, correlates well with the elevation of the surface of soft rock from the drill core and SPS logging data. This cost-effective routine developed for quickly delineating the bedrock surface in the field survey will be readily applicable to mapping of weathering zone in narrow zone with small variation of elevation of bedrock.

• Journal of KIISE
• /
• v.44 no.6
• /
• pp.643-648
• /
• 2017

Among Wi-Fi based indoor positioning systems, fingerprinting localization is the most common technique with high precision. However, construction of the initial radio map and the update process require considerable labor and time effort. To address this problem, we propose an efficient method that constructs the initial radio map at each vertex based on a graph. In addition, we introduce a method to update the radio map automatically by mapping signal data acquired from users to the reference point created on each edge. Since the proposed method collects signal data manually only at the vertex of the graph to build the initial radio map and updates it automatically, our proposed method can dramatically reduce labor and time effort, which are the disadvantages of the conventional fingerprinting method. In our experimental study, we show validity of our radio map update method by comparing with the actual reference point data. We also show that our proposed method is able to construct the radio map with an accuracy of about 3.5m by automatically updating the radio map.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.10
• /
• pp.833-838
• /
• 2016

This paper presents a performance evaluation of various feature-initialization algorithms for forward-viewing mono-camera based simultaneous localization and mapping (SLAM), specifically in indoor environments. For mono-camera based SLAM, the position of feature points cannot be known from a single view; therefore, it should be estimated from a feature initialization method using multiple viewpoint measurements. The accuracy of the feature initialization method directly affects the accuracy of the SLAM system. In this study, four different feature initialization algorithms are evaluated in simulations, including linear triangulation; depth parameterized, linear triangulation; weighted nearest point triangulation; and particle filter based depth estimation algorithms. In the simulation, the virtual feature positions are estimated when the virtual robot, containing a virtual forward-viewing mono-camera, moves forward. The results show that the linear triangulation method provides the best results in terms of feature-position estimation accuracy and computational speed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.3
• /
• pp.175-185
• /
• 2020

Three-dimensional registration is a process of matching data with or without a coordinate system to a reference coordinate system, which is used in various fields such as the absolute orientation of photogrammetry and data combining for producing precise road maps. Three-dimensional registration is divided into a method using points and a method using linear features. In the case of using points, it is difficult to find the same conjugate point when having different spatial resolutions. On the other hand, the use of linear feature has the advantage that the three-dimensional registration is possible by using not only the case where the spatial resolution is different but also the conjugate linear feature that is not the same starting point and ending point in point cloud type data. In this study, we proposed a method to determine the scale and the three-dimensional translation after determining the three-dimensional rotation angle between two data using quaternion to perform three-dimensional registration using linear features. For the verification of the proposed method, three-dimensional registration was performed using the linear features constructed an indoor and the linear features acquired through the terrestrial mobile mapping system in an outdoor environment. The experimental results showed that the mean square root error was 0.001054m and 0.000936m, respectively, when the scale was fixed and if not fixed, using indoor data. The results of the three-dimensional transformation in the 500m section using outdoor data showed that the mean square root error was 0.09412m when the six linear features were used, and the accuracy for producing precision maps was satisfied. In addition, in the experiment where the number of linear features was changed, it was found that nine linear features were sufficient for high-precision 3D transformation through almost no change in the root mean square error even when nine linear features or more linear features were used.

• Journal of Internet Computing and Services
• /
• v.19 no.5
• /
• pp.43-54
• /
• 2018

Images taken in haze weather are characteristic of low contrast and poor visibility. The process of reconstructing clear-weather image from a hazy image is called dehazing. The main challenge of image dehazing is to estimate the transmission map or depth map for an input hazy image. In this paper, we propose a single image dehazing method by utilizing the Generative Adversarial Network(GAN) for accurate depth map estimation. The proposed GAN model is trained to learn a nonlinear mapping between the input hazy image and corresponding depth map. With the trained model, first the depth map of the input hazy image is estimated and used to compute the transmission map. Then a guided filter is utilized to preserve the important edge information of the hazy image, thus obtaining a refined transmission map. Finally, the haze-free image is recovered via atmospheric scattering model. Although the proposed GAN model is trained on synthetic indoor images, it can be applied to real hazy images. The experimental results demonstrate that the proposed method achieves superior dehazing results against the state-of-the-art algorithms on both the real hazy images and the synthetic hazy images, in terms of quantitative performance and visual performance.