• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.86-88
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• 2005

An autonomous semantic-map building method is proposed, with the robot localized in the semantic-map. Our semantic-map is organized by objects represented as SIFT features and vision-based relative localization is employed as a process model to implement extended Kalman filters. Thus, we expect that robust SLAM performance can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based SLAM

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.164-172
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• 2023

In this study, we identify the recognition of 'The First Slam Dunk', which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of 'The First Slam Dunk' in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as 'The First Slam Dunk' was the keyword with the high frequency among the frequent keywords. Next are 'Slam Dunk', 'Movie', 'Premiere', 'Animation', 'Audience', and 'Box-Office'. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of 'The First Slam Dunk', basic data on the development plan of sports content were provided.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1073-1080
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• 2024

The imperative for real-time automatic construction progress monitoring (ACPM) to avert project delays is widely acknowledged in construction project management. Current ACPM methodologies, however, face a challenge as they rely on collecting data from construction sites and processing it offline for progress analysis. This delayed approach poses a risk of late identification of critical construction issues, potentially leading to rework and subsequent project delays. This research introduces a real-time construction progress monitoring framework that integrates cutting-edge semantic Simultaneous Localization and Mapping (SLAM) techniques. The innovation lies in the framework's ability to promptly identify structural components during site inspections conducted through a robotic system. Incorporating deep learning models, specifically those employing semantic segmentation, enables the system to swiftly acquire and process real-time data, identifying specific structural components and their respective locations. Furthermore, by seamlessly integrating with Building Information Modeling (BIM), the system can effectively evaluate and compare the progress status of each structural component. This holistic approach offers an efficient and practical real-time progress monitoring solution for construction projects, ensuring timely issue identification and mitigating the risk of project delays.

• Annual Conference of KIPS
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• 2023.11a
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• pp.848-849
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• 2023

본 논문은 로봇의 실내 환경에서의 자율성을 높이기 위한 SLAM과 내비게이션 방법을 제시한다. 2D LiDAR와 카메라를 이용하여 위치를 인식하고 사람과 장애물을 의미론적으로 검출하며, ICP와 RTAB-map, YOLOv3를 통합하여 Semantic Map을 생성하고 실내 환경에서 자율성을 유지한다. 이 연구를 통해 로봇이 복잡한 환경에서도 높은 수준의 자율성을 유지할 수 있는지 확인하고자 한다.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.334-338
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• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.