• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.130-138
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• 2024

High-precision 3D Object Detection is a crucial component within autonomous driving systems, with far-reaching implications for subsequent tasks like multi-object tracking and path planning. In this paper, we propose a novel approach designed to enhance the performance of 3D Object Detection, especially in heading angle estimation by employing a moving object segmentation technique. Our method starts with extracting point-wise moving labels via a process of moving object segmentation. Subsequently, these labels are integrated into the LiDAR Pointcloud data and integrated data is used as inputs for 3D Object Detection. We conducted an extensive evaluation of our approach using the KITTI-road dataset and achieved notably superior performance, particularly in terms of AOS, a pivotal metric for assessing the precision of 3D Object Detection. Our findings not only underscore the positive impact of our proposed method on the advancement of detection performance in lidar-based 3D Object Detection methods, but also suggest substantial potential in augmenting the overall perception task capabilities of autonomous driving systems.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.106-116
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• 2024

Ensuring robust 3D object detection is a core challenge for autonomous driving systems operating in urban environments. To tackle this issue, various 3D representation, including point cloud, voxels, and pillars, have been widely adopted, making use of LiDAR, Camera, and Radar sensors. These representations improved 3D object detection performance, but real-world urban scenarios with unexpected situations can still lead to numerous false positives, posing a challenge for robust 3D models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. While conventional perception algorithms typically employ a single threshold in post-processing, 3D models perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. The proposed algorithm tackles this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in the 3D model. The results show performance enhancements in the 3D model across a range of scenarios, encompassing not only typical urban road conditions but also scenarios involving adverse weather conditions.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.637-641
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• 2007

Airborne LIDAR (Light Detection and Ranging) technology has reached a degree of the required accuracy in mapping professions, and advanced LIDAR systems are becoming increasingly common in the various fields of application. LiDAR data constitute an excellent source of information for reconstructing the Earth's surface due to capability of rapid and dense 3D spatial data acquisition with high accuracy. However, organizing the LIDAR data and extracting information from the data are difficult tasks because LIDAR data are composed of randomly distributed point clouds and do not provide sufficient semantic information. The main reason for this difficulty in processing LIDAR data is that the data provide only irregularly spaced point coordinates without topological and relational information among the points. This study introduces an efficient and robust method for automatic extraction of building footprints using airborne LIDAR data. The proposed method separates ground and non-ground data based on the histogram analysis and then rearranges the building boundary points using convex hull algorithm to extract building footprints. The method was implemented to LIDAR data of the heavily built-up area. Experimental results showed the feasibility and efficiency of the proposed method for automatic producing building layers of the large scale digital maps and 3D building reconstruction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.326-331
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• 2019

In the real world, there are both dynamic objects and static objects, but an autonomous vehicle or mobile robot cannot distinguish between them, even though a human can distinguish them easily. It is important to distinguish static objects from dynamic objects clearly to perform autonomous driving successfully and stably for an autonomous vehicle or mobile robot. To do this, various sensor systems can be used, like cameras and LiDAR. Stereo camera images are used often for autonomous driving. The stereo camera images can be used in object recognition areas like object segmentation, classification, and tracking, as well as navigation areas like 3D world reconstruction. This study suggests a method to distinguish static/dynamic objects using stereo vision for an online autonomous vehicle and mobile robot. The method was applied to a 3D world map reconstructed from stereo vision for navigation and had 99.81% accuracy.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.691-702
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• 2019

Flood inundation map has various important roles in terms of municipal planning, timely dam operation, economic levee design, and building flood forecasting systems. Considering that the riparian areas adjacent to national rivers with high potential flood vulnerability conventionally imposed special cares to justify applications of recently available two- or three-dimensional flood inundation numerical models on top of digital elevation models of dense spatial resolution such as LiDAR irrespective of their high costs. On the contrary, local streams usually could not have benefits from recent technological advances, instead they inevitably have relied upon time-consuming manual drawings or have accepted DEMs with poor resolutions or inaccurate 1D numerical models for producing inundation maps due mainly to limited budgets and suitable techniques. In order to efficiently and cost-effectively provide a series of flood inundation maps dedicatedly for the local streams, this study proposed an OpenGIS-based flood mapping tool named Open Flood Mapper (OFM). The spatial accuracy of flood inundation map derived from the OFM was validated throughout comparison with an inundation trace map acquired after typhoon Nari in Hancheon basin located in Jeju Island. Also, a series of inundation maps from the OFM were comprehensively investigated to track the burst of flood in the extreme flood events.