• 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.18 no.3
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.

• Korean Journal of Construction Engineering and Management
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• v.17 no.3
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• pp.32-42
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• 2016

In Korea, the absence of BIM use in existing civil structures and buildings is driving a demand for as-built BIM. As-built BIMs are often created using laser scanners that provide dense 3D point cloud data. Conventional static laser scanning approaches often suffer from limitations in their operability due to the difficulties in moving the equipment, the selection of scanning location, and the requirement of placing targets or extracting tie points for registration of each scanned point cloud. This paper aims at reducing the manual effort using a kinematic 3D laser scanning system based on graph-based simultaneous localization and mapping (SLAM) for continuous indoor mapping. The robotic platform carries three 2D laser scanners: the front scanner is mounted horizontally to compute the robot's trajectory and to build the SLAM graph; the other two scanners are mounted vertically to scan the profiles of surrounding environments. To reduce the accumulated error in the trajectory of the platform through loop closures, the graph-based SLAM system incorporates AdaBoost loop closure approach, which is particularly suitable for the developed multi-scanner system providing more features than the single-scanner system for training. We implemented the proposed method and evaluated it in two indoor test sites. Our experimental results show that the false positive rate was reduced by 13.6% and 7.9% for the two dataset. Finally, the 2D and 3D mapping results of the two test sites confirmed the effectiveness of the proposed graph-based SLAM.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.5
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• pp.465-470
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• 2015

This paper proposes a robust 3D mapping system for a UAV (Unmanned Aerial Vehicle) that carries a LRF (Laser Range Finder) using the sinusoidal trajectory algorithm. In the case of previous 3D mapping research, the UAV usually takes off vertically and flights up and down while the LRF is measuring horizontally. In such cases, the measuring range is limited and it takes a long time to do mapping. By using the sinusoidal trajectory algorithm proposed in this research, the 3D mapping can be time-efficient and the measuring range can be widened. The 3D mapping experiments have been done to evaluate the performance of the sinusoidal trajectory algorithm by scanning indoor walls.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.91-101
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• 2014

This study tried to create 3D object with LOD3 level for Korean traditional house which is atypical structure, upload to spatial information open platform and confirm the possibility for creating 3D-map. And this study tried to create 3D model for Korean traditional house based on BIM, performed 3D modeling for interior spatial information of Korean traditional house and confirm the development possibility of 3D modeling and visualization method of Korean traditional house. Also this study present the possibility of LOD4 level visualization for spatial information of Korean traditional house which is atypical structure, but 3D object with LOD4 level can't be uploaded to Spatial Information Open Platform currently, cause by data volume limitation of spatial information open platform.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.509-516
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• 2022

Accurate pose prediction of objects in 3D space is an important visual recognition technique widely used in many applications such as scene understanding in both indoor and outdoor environments, robotic object manipulation, autonomous driving, and augmented reality. Most previous works for object pose estimation have the limitation that they require an exact 3D CAD model for each object. Unlike such previous works, this paper proposes a novel neural network model that can predict the poses of unknown objects based on only their RGB color images without the corresponding 3D CAD models. The proposed model can obtain depth maps required for unknown object pose prediction by using an adaptive depth estimator, AdaBins,. In this paper, we evaluate the usefulness and the performance of the proposed model through experiments using benchmark datasets.

• 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 Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.40-47
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• 2010

This paper presents a simultaneous localization and mapping (SLAM) of an indoor hallway environment using Rao-Blackwellized particle filter (RBPF) along with a line segment as a landmark. Based on the fact that fluent line features can be extracted around the ceiling and side walls of hallway using vision sensor, a horizontal line segment is extracted from an edge image using Hough transform and is also tracked continuously by an optical flow method. A successive observation of a line segment gives initial state of the line in 3D space. For data association, registered feature and observed feature are matched in image space through a degree of overlap, an orientation of line, and a distance between two lines. Experiments show that a compact environmental map can be constructed with small number of horizontal line features in real-time.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.348-353
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• 2007

사회구조가 고도화됨에 따라서 사용자들은 크고 복잡한 환경에 노출되고 있다. 이러한 환경은 사용자들이 자신의 위치가 어디인지를 인식하게 어렵게 하고 있다. 그에 따라서 사용자에게 지도나 키오스크 시스템[1]과 같은 위치 안내 서비스를 제공하고 있다. 하지만 이런 서비스는 2D 기반의 안내를 하기 때문에 사용자가 직관적, 공간적으로 자신의 위치를 파악하기란 어렵다. 직접 시스템에 찾아가야 서비스를 사용할 수 있으며, 서비스의 제공이 지속적이지 않고, 순간적이라는 단점이 있다. 본 논문에서는 사용자가 이동하면서 자신이 찾고자 하는 위치에 대한 안내를 3D 가상현실 기반의 위치 안내를 받을 수 있는 모바일 PC를 위한 위치 기반 서비스(Location Based Service)를 제공한다. 제안하는 시스템은 사용자의 현재의 위치를 인식하기 위해서 모바일 PC에 GPS와 RFID태그를 이용한다. 이를 실내외 공간에서의 위치를 인식을 하게 되며, 사용자의 현재 위치에서 자신이 가고자 하는 목표장소에 대한 최단 경로를 알려 준다. 뿐만 아니라 여러 장소를 방문하는 경우, 미리 만든 스케쥴에 따라서 위치 안내 서비스를 받을 수 있다. 제안하는 시스템은 사용자가 전시장, 병원, 관공서 등의 건물에서 원하는 위치를 자신이 있는 위치에서 편하고 간편하게 찾아 줄 수 있으며, 부가적으로 다양한 위치 기반 서비스들이 적용 가능하다.