• Aerospace Engineering and Technology
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• v.11 no.2
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• pp.140-148
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• 2012

For autonomous navigation, map, pose tracking, and finding the shortest path are required. Because there is no GPS signal in indoor environment, the current position should be recognized in the 3D map by using image processing or something. In this paper, we explain 3D map creation technology by using depth camera like Kinect and pose tracking in 3D map by using 2D image taking from camera. In addition, the mechanism of avoiding obstacles is discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.745-752
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• 2016

We suggest an efficient Simultaneous Localization and 3D Polygon Map Building (SLAM) method with Kinect depth sensor for mobile robots in indoor environments. In this method, Kinect depth data is separated into row planes so that scan line segments are on each row plane. After grouping all scan line segments from all row planes into line groups, a set of 3D Scan polygons are fitted from each line group. A map matching algorithm then figures out pairs of scan polygons and existing map polygons in 3D, and localization is performed to record correct pose of the mobile robot. For 3D map-building, each 3D map polygon is created or updated by merging each matched 3D scan polygon, which considers scan and map edges efficiently. The validity of the proposed 3D SLAM algorithm is revealed via experiments.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.25-31
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• 2022

Moving among multiple floors is one of the most challenging tasks for indoor autonomous robots. Most of the previous researches for indoor mapping and localization have focused on singular floor environment. In this paper, we present an algorithm that creates a multi-floor map using 3D point cloud. We implement localization within the multi-floor map using a LiDAR and an IMU. Our algorithm builds a multi-floor map by constructing a single-floor map using a LOAM-based algorithm, and stacking them through global registration that aligns the common sections in the map of each floor. The localization in the multi-floor map was performed by adding the height information to the NDT (Normal Distribution Transform)-based registration method. The mean error of the multi-floor map showed 0.29 m and 0.43 m errors in the x, and y-axis, respectively. In addition, the mean error of yaw was 1.00°, and the error rate of height was 0.063. The real-world test for localization was performed on the third floor. It showed the mean square error of 0.116 m, and the average differential time of 0.01 sec. This study will be able to help indoor autonomous robots to operate on multiple floors.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.2
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• pp.133-138
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• 2011

RFID system can be used to improve object recognition, map building and localization for robot area. A novel method of indoor navigation system for a mobile robot is proposed using RFID technology. The mobile robot With a RFID reader and antenna is able to find what obstacles are located where in circumstance and can build the map similar to indoor circumstance by combining RFID information and distance data obtained from sensors. Using the map obtained, the mobile robot can avoid obstacles and finally reach the desired goal by $A^*$ algorithm. 3D map which has the advantage of robot navigation and manipulation is able to be built using z dimension of products. The proposed robot navigation system is proved to apply for SLAM and path planning in unknown circumstance through numerous experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.153.5-153
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• 2001

This paper presents a simple modeling system that constructs 3D models from an indoor cylindrical environment map using all of the available geometry of the interior structure such as vertical and horizontal lines and parallel and perpendicular planes. The indoor scene abstract model is created through this system and the navigation through the process of 3D reconstruction. This system first automatically detects the vanishing points in a cylindrical environment map from parallel lines and planes, and determines the indoor scene topology previously defined using this information. The determined topology enables he user intervention UI simply construct a 3D model by using the photogrammetry. The modeling system can be ...

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.169-175
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• 2016

Geofence is a virtual perimeter for a real-world geographical area, which could be statically or dynamically established the specified area if necessary. Many geofencing applications incorporate 2D(two-dimensional) map such as the Google map, allowing administrators to define boundaries on top of a satellite view of a specific geographical area. But these applications do not provide 3D(three-dimensional) spatial information as well as 2D location information no matter where indoor or outdoor. Therefore we propose a mechanism to identify indoor or outdoor location for 3D geofence, and implement 3D geofence using smartphone. The proposed mechanism identifies the position information on 3D geofence regardless of indoor or outdoor, inter-floor with only GPS and WiFi. In the near future, 3D geofence as well as LBS are promising applications that become possible when IoT can become organized and connected by location.

• Journal of Ubiquitous Convergence Technology
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• v.2 no.2
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• pp.67-77
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• 2008

Representation of various types of information in an interactive virtual reality environment on mobile devices had been an attractive and valuable research in this new era. Our main focus is presenting spatial indoor location sensing information in 3D perception in mind to replace the traditional 2D floor map using handheld PDA. Designation of 3D virtual reality by Virtual Reality Modeling Language (VRML) demonstrates its powerful ability in providing lots of useful positioning information for PDA user in real-time situation. Furthermore, by interpolating portal culling algorithm would reduce the 3D graphics rendering time on low power processing PDA significantly. By fully utilizing the CC2420 chipbased sensor nodes, wireless sensor network was established to locate user position based on Received Signal Strength Indication (RSSI) signals. Implementation of RSSI-based indoor tracking method is low-cost solution. However, due to signal diffraction, shadowing and multipath fading, high accuracy of sensing information is unable to obtain even though with sophisticated indoor estimation methods. Therefore, low complexity and flexible accuracy refinement algorithm was proposed to obtain high precision indoor sensing information. User indoor position is updated synchronously in virtual reality to real physical world. Moreover, assignment of magnetic compass could provide dynamic orientation information of user current viewpoint in real-time.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.355-361
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• 2006

In large scale environments like airport, museum, large warehouse and department store, autonomous mobile robots will play an important role in security and surveillance tasks. Robotic security guards will give the surveyed information of large scale environments and communicate with human operator with that kind of data such as if there is an object or not and a window is open. Both for visualization of information and as human machine interface for remote control, a 3D model can give much more useful information than the typical 2D maps used in many robotic applications today. It is easier to understandable and makes user feel like being in a location of robot so that user could interact with robot more naturally in a remote circumstance and see structures such as windows and doors that cannot be seen in a 2D model. In this paper we present our simple and easy to use method to obtain a 3D textured model. For expression of reality, we need to integrate the 3D models and real scenes. Most of other cases of 3D modeling method consist of two data acquisition devices. One for getting a 3D model and another for obtaining realistic textures. In this case, the former device would be 2D laser range-finder and the latter device would be common camera. Our algorithm consists of building a measurement-based 2D metric map which is acquired by laser range-finder, texture acquisition/stitching and texture-mapping to corresponding 3D model. The algorithm is implemented with laser sensor for obtaining 2D/3D metric map and two cameras for gathering texture. Our geometric 3D model consists of planes that model the floor and walls. The geometry of the planes is extracted from the 2D metric map data. Textures for the floor and walls are generated from the images captured by two 1394 cameras which have wide Field of View angle. Image stitching and image cutting process is used to generate textured images for corresponding with a 3D model. The algorithm is applied to 2 cases which are corridor and space that has the four wall like room of building. The generated 3D map model of indoor environment is shown with VRML format and can be viewed in a web browser with a VRML plug-in. The proposed algorithm can be applied to 3D model-based remote surveillance system through WWW.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.67-79
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• 2017

Increasing transportation dependence on the metro system has lead to the convenience of passengers becoming as important as the transportation capacity. In this study, a pedestrian simulator has been developed that can quantitatively assess the pedestrian environment in terms of attributes such as speed and distance. The simulator consists of modules designed for 3D indoor map authoring and algorithmic pedestrian modeling. Module functions for 3D indoor map authoring include 3D spatial modeling, network generation, and evaluation of obtained results. The pedestrian modeling algorithm executes functions such as conducting a path search, allocation of users, and evaluation of level of service (LOS). The primary objective behind developing the said functions is to apply and analyze various scenarios repeatedly, such as before and after the improvement of the pedestrian environment, and to integrate the spatial information database with the dynamic information database. Furthermore, to demonstrate the practical applicability of the proposed simulator in the future, a test-bed was constructed for a currently operational metro station and the quantitative index of the proposed improvement effect was calculated by analyzing the walking speed of pedestrians before and after the improvement of the passage. The possibility of database extension for further analysis has also been discussed in this study.