• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.1-123
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• 2001

For Autonomous navigation of the mobile robots, the robots' capability to recognize 3D environment is necessary. In this paper, an on-line 3D map building method for autonomous mobile robots is proposed. To get range data on the environment, we use an sensor system which is composed of a structured light and a CCD camera based on optimal triangulation. The structured laser is projected as a horizontal strip on the scene. The sensor system can rotate $\pm$ $30{\Circ}$ with a goniometer. Scanning the system, we get the laser strip image for the environments and update planes composing the environment by some image processing steps. From the laser strip on the captured image, we find a center point of each column, and make line segments through blobbing these center poings. Then, the planes of the environments are updated. These steps are done on-line in scanning phase. With the proposed method, we can efficiently get a 3D map about the structured environment.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.123.5-123
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• 2001

For autonomous navigation of the mobile robots, the robots' capability to recognize 3D environment is necessary. In this paper, an on-line 3D map building method for autonomous mobile robots is proposed. To get range data on the environment, we use a sensor system which is composed of a structured light and a CCD camera based on optimal triangulation. The structured laser is projected as a horizontal strip on the scene. The sensor system can rotate$\pm$30$^{\circ}$ with a goniometer. Scanning the system, we get the laser strip image for the environments and update planes composing the environment by some image processing steps. From the laser strip on the captured image, we find a center point of each column, and make line segments through blobbing these center points. Then, the planes of the environments are updated. These steps are done on-line in scanning phase. With the proposed method, we can efficiently get a 3D map about the structured environment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.539-546
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• 2005

This paper describes a method of building the probability grid map for an autonomous mobile robot using the ultrasonic DAF(data association filter). The DAF, which evaluates the association of each data with the rest and removes the data affected by the specular reflection effect, can improve the reliability of the data for the Probability grid map. This method is based on the evaluation of possibility that the acquired data are all from the same object. Namely, the data from specular reflection have very few possibilities of detecting the same object, so that they are excluded from the data cluster during the process of the DAF. Therefore, the uncertain data corrupted by the specular reflection and/or multi-path effect, are not used to update the probability map, and hence building a good quality of a grid map is possible even in a specular environment. In order to verify the effectiveness of the DAF, it was applied to the Bayesian model and the orientation probability model which are the typical ones of a grid map. We demonstrate the experimental results using a real mobile robot in the real world.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.79-85
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• 2018

This study focuses on autonomous exploration based on map expansion for an underwater robot equipped with acoustic sonars. Map expansion is applicable to large-area mapping, but it may affect localization accuracy. Thus, as the key contribution of this paper, we propose a method for underwater autonomous exploration wherein the robot determines the trade-off between map expansion ratio and position accuracy, selects which of the two has higher priority, and then moves to a mission step. An occupancy grid map is synthesized by utilizing the measurements of an acoustic range sonar that determines the probability of occupancy. This information is then used to determine a path to the frontier, which becomes the new search point. During area searching and map building, the robot revisits artificial landmarks to improve its position accuracy as based on imaging sonar-based recognition and EKF-SLAM if the position accuracy is above the predetermined threshold. Additionally, real-time experiments were conducted by using an underwater robot, yShark, to validate the proposed method, and the analysis of the results is discussed herein.

• The Journal of Korea Robotics Society
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• v.7 no.4
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• pp.252-258
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• 2012

This paper describes hierarchical semantic map building using the classified area information in home environments. The hierarchical semantic map consists of a grid, CAIG (Classified Area Information in Grid), and topological map. The grid and CAIG maps are used for navigation and motion selection, respectively. The topological map provides the intuitive information on the environment, which can be used for the communication between robots and users. The proposed semantic map building algorithm can greatly improve the capabilities of a mobile robot in various domains, including localization, path-planning and HRI (Human-Robot Interaction). In the home environment, a door can be used to divide an area into various sections, such as a room, a kitchen, and so on. Therefore, we used not only the grid map of the home environment, but also the door information as a main clue to classify the area and to build the hierarchical semantic map. The proposed method was verified through various experiments and it was found that the algorithm guarantees autonomous map building in the home environment.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1184-1189
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• 2003

Map building and position estimation capabilities are practically indispensable for a mobile robot to execute its given tasks in its working environments. An autonomous map building method and a smart localization method is proposed in our previous works. The experimental verifications are carried out in this paper. We applied the proposed algorithms to mobile service robots in large-scale indoor buildings. Experimental results show that our strategy is reliable and feasible in tough conditions like non-polygonal and dynamic environments. The advantages of the algorithms are well-illustrated through real experiments.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.9 no.1
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• pp.102-110
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• 2000

This paper presents an algorithm of local map building for autonomous robot navigation using LASER range finder information. We develop a model of sensor output for a LASER range finder, and obtain an output data of the LASER range finder for a given environment. From the output data, a local map is obtained through the following procedures: (1) filtering of output data to remove noisy and unnecessary data, (2) comparison of filtered data with the original data to restore useful data, (3) thickening of the map obtained from the restored data, and (4) skeletonizing of the thickened map to get a final local map. Through some simulation studies, a map is obtained from the LASER range finder information for a given indoor environment, and is compared with the environment.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.330-334
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• 2001

This paper presents the way of building an occupancy grid map which a mobile robot needs to autonomously navigate in the unknown environment. A disparity map resulting from stereo matching can be converted into the 2D distance information. If the stereo matching has some errors, however, the subsequent map becomes unreliable. In this paper, a new morphological filter is proposed to reject 'spikes' of the disparity map due to stereo mismatch by considering the fact that these spikes occur locally. The new method has advantages that it is simpler and more easily realized than existing similar algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1437-1443
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• 2009

This paper is presented an simultaneous localization and mapping (SLAM) algorithm using ultrasonic for robot and electric compass, encoder, and gyro. Generally, localization based upon electric compass, encoder, and gyro can be measured just local position in workspace. However, actual robot must need an information of the absolute position in workspace to perform its mission, Absolute position in workspace could be calculated using SLAM algorithm. To implement SLAM in this paper, a map is built using ultrasonic sensor and hierarchical map building method. And then, we the map will be transformed into a feature map. The absolute position could be calculated using the feature map and map mapping method. As a test bed, we designed and construct an autonomous robot and showed the experimental performance of the proposed SLAM algorithm based on feature map. Experimental result, we verified that robot can found all absolute position on experiments using proposed SLAM algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.102-116
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• 2021

For the safe operation of autonomous vehicles, the representative technology of the 4^th industrial revolution era, a combination of various technologies such as sensor technology, software technology and car technology is required. An autonomous vehicle is a vehicle that recognizes current location and situation by using the various sensors, and makes its own decisions without depending on the driver. Perfect recognition technology is required for fully autonomous driving. Since the precise road maps provide various road information including lanes, stop lines, traffic lights and crosswalks, it is possible to minimize the cognitive errors that occur in autonomous vehicles by using the precise road maps with location information of the road facilities. In this study, the definition, necessity and technical trends of the precise road map have been analyzed, and the HD(High Definition) map prototype based on the web GIS has been built in the autonomous driving-specialized areas of Daegu Metropolitan City(Suseong Medical District, about 24km), the Happy City of Sejong Special Self-Governing City(about 33km), and the FMTC(Future Mobility Technical Center) PG(Proving Ground) of Seoul National University Siheung Campus using the MMS(Mobile Mapping System) surveying results given by the National Geographic Information Institute. In future research, the built-in precise road map service will be installed in the autonomous vehicles and control systems to verify the real-time locations and its location correction algorithm.