• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.389-401
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• 2011

Most nations around the world have expressed significant concern in the climate change due to a rapid increase in green-house gases and thus reach an international agreement to control total amount of these gases for the mitigation of global warming. As the most important absorber of carbon dioxide, one of major green-house gases, forest resources should be more tightly managed with a means to measure their total amount, forest biomass, efficiently and accurately. Forest biomass has close relations with forest areas and tree height. Airborne LiDAR data helps extract biophysical properties on forest resources such as tree height more efficiently by providing detailed spatial information about the wide-range ground surface. Many researchers have thus developed various methods to estimate tree height using LiDAR data, which retain different performance and characteristics depending on forest environment and data characteristics. In this study, we attempted to investigate such various techniques to estimate tree height, elaborate their advantages and limitations, and suggest future research directions. We first examined the characteristics of LiDAR data applied to forest studies and then analyzed methods on filtering, a precedent procedure for tree height estimation. Regarding the methods for tree height estimation, we classified them into two categories: individual tree-based and regression-based method and described the representative methods under each category with a summary of their analysis results. Finally, we reviewed techniques regarding data fusion between LiDAR and other remote sensing data for future work.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.3
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• pp.401-408
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• 2009

A study for 3D-reconstruction and providing the geospatial information is in progress to many fields recently. For efficient providing the geospatial information, the present information has to be updated and be revised and then the latest geospatial information needs to be acquired economically. Especially, LiDAR system utilized in many study has a advantage to collect the 3D spacial data easily and densely that is possible to supply to the geospatial information. The 3D data of LiDAR is very suitable as a data for presenting 3D space, but in case of using the data without converting, the high performance processor is needed for presenting 2D forms from point data composed by 3D data. In comparison, basically the raster data structure of 2D form is more efficient than vector structure in cheap devices because of a simple structure and process speed. The purpose of this study, in case of supplying LiDAR data as 3D data, present the method that reconstructs to 2D raster data and convert to compression data applied by th tree construction in detail.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.6_3
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• pp.1147-1154
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• 2022

Now that the development of autonomous driving is progressing, LiDAR has become an indispensable element. However, LiDAR is a device that uses lasers, and laser side effects may occur. One of them is the much-talked-about eye-safety, and developers have been satisfying this through laser characteristics and operation methods. But eye-safety is just one of the problems lasers pose. For example, irradiating a laser with a specific energy level or higher in a dusty environment can cause deterioration of the dust particles, leading to a sudden explosion. For this reason, the dust ignition proof regulations clearly state that "a source with a pulse period of less than 5 seconds is considered a continuous light source, and the average energy does not exceed 5 mJ/mm 2 or 35 mW" [2]. Energy of output optical power is limited by the law. In this way, the manufacturer cannot define the usage environment of the LiDAR, and the development of a LiDAR that can be used in such an environment can increase the ripple effect in terms of use in application fields using the LiDAR. In this paper, we develop a LiDAR with low optical power that can be used in environments where high power lasers can cause problems, evaluate its performance. Also, we discuss and present one of the directions for the development of LiDAR with laser power limited by dust ignition proof regulations.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.123-128
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• 2021

This paper present box feature estimation from LiDAR point cluster using maximum likelihood Method. Previous LiDAR tracking method for autonomous driving shows high accuracy about velocity and heading of point cluster. However, Assuming the average position of a point cluster as the vehicle position has a lower accuracy than ground truth. Therefore, the box feature estimation algorithm to improve position accuracy of autonomous driving perception consists of two procedures. Firstly, proposed algorithm calculates vehicle candidate position based on relative position of point cluster. Secondly, to reflect the features of the point cluster in estimation, the likelihood of the particle scattered around the candidate position is used. The proposed estimation method has been implemented in robot operating system (ROS) environment, and investigated via simulation and actual vehicle test. The test result show that proposed cluster position estimation enhances perception and path planning performance in autonomous driving.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.397-406
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• 2022

This paper presents an autonomous mobile robot (AMR) system and operation algorithms for logistic and factory facilities without magnet-lines installation. Unlike widely used AMR systems, we propose an EKF-based loosely coupled fusion of LiDAR measurements and visual markers. Our method first constructs occupancy grid and visual marker map in the mapping process and utilizes prebuilt maps for precise localization. Also, we developed a waypoint-based navigation pipeline for robust autonomous operation in unconstrained environments. The proposed system estimates the robot pose using by updating the state with the fusion of visual marker and LiDAR measurements. Finally, we tested the proposed method in indoor environments and existing factory facilities for evaluation. In experimental results, this paper represents the performance of our system compared to the well-known LiDAR-based localization and navigation system.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.81-94
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• 2013

The Radar altimeter transmits radio signals to the surface, receives the backscattered signals and measures the distance between the airplane and the nadir surface. The measurements of radar altimeter are affected by various factors on the surface below the aircraft. This study performed flight campaigns in June 2012 and acquired raw data from radar altimeter, LiDAR and other sensors. Based on the LiDAR DSM (Digital Surface Model) as a reference data, the characteristics of radar altimeter were analyzed in the respect of range and surface area affecting on the receiving power of the radar altimeter. Consequently, the radar altimeter was strongly affected by the surface area within beam width and reflectivity related to RCS (Radar Cross Section) rather than range.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.9-15
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• 2011

Nowadays, the Surveying field is growing rapidly in terms of technology such as TS(Total Station) surveying, photographic surveying, digital aerial photogrammetry, utilization of GIS(Geographic Information System) using high-resolution satellite imagery, obtaining 3D Coordinate using GPS. But control point surveying, benchmark measuring, and field Surveying are still performed by the engineers in the field. So, 3D yerrestrial laser scanner comes to the fore recently. 3D terrestrial laser scanner can get 3D coordinate about a number of sites of the subject in a short period with high accuracy. This paper compared the accuracy of data from the performance using 3D terrestrial laser scanner with that of TS. It also obtained the geopositioning accuracy result equivalent to the surveying result of TS. With further researches in the future, it is expected to be used not only in LiDAR itself but also in various areas like reconnaissance Surveying and construction by combining with TS or other Surveying equipments.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.91-102
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• 2017

Backpack-mounted mapping system is firstly introduced for flexible movement in indoor spaces where satellite-based localization is not available. With the achieved advances in miniaturization and weight reduction, use of LiDAR (Light Detection and Ranging) sensors in mobile platforms has been increasing, and indeed, they have provided high-precision information on indoor environments and their surroundings. Previous research on the development of backpack-mounted mapping systems, has concentrated mostly on the improvement of data processing methods or algorithms, whereas practical system components have been determined empirically. Thus, in the present study, a simulator for a LiDAR sensor (Velodyne VLP-16), was developed for comparison of the effects of diverse conditions on the backpack system and its operation. The simulated data was analyzed by visual inspection and comparison of the data sets' statistics, which differed according to the LiDAR arrangement and moving speed. Also, the data was used as input to a point-cloud registration algorithm, ICP (Iterative Closest Point), to validate its applicability as pre-analysis data. In fact, the results indicated centimeter-level accuracy, thus demonstrating the potentials of simulation data to be utilized as a tool for performance comparison of pointdata processing methods.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.780-789
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• 2019

In this paper, we propose a new indoor localization method for indoor mobile robots using LiDAR. The indoor mobile robots operating in limited areas usually require high-precision localization to provide high level services. The performance of the widely used localization methods based on radio waves or computer vision are highly dependent on their usage environment. Therefore, the reproducibility of the localization is insufficient to provide high level services. To overcome this problem, we propose a new localization method based on the comparison between ceiling shape information obtained from LiDAR measurement and the blueprint. Specifically, the method includes a reliable segmentation method to classify point clouds into connected planes, an effective comparison method to estimate position by matching 3D point clouds and 2D blueprint information. Since the ceiling shape information is rarely changed, the proposed localization method is robust to its usage environment. Simulation results prove that the position error of the proposed localization method is less than 10 cm.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.580-591
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• 2019

This paper focuses on to improving object detection performance using the camera and LiDAR on autonomous vehicle platforms by fusing detected objects from individual sensors through a late fusion approach. In the case of object detection using camera sensor, YOLOv3 model was employed as a one-stage detection process. Furthermore, the distance estimation of the detected objects is based on the formulations of Perspective matrix. On the other hand, the object detection using LiDAR is based on K-means clustering method. The camera and LiDAR calibration was carried out by PnP-Ransac in order to calculate the rotation and translation matrix between two sensors. For Sensor fusion, intersection over union(IoU) on the image plane with respective to the distance and angle on world coordinate were estimated. Additionally, all the three attributes i.e; IoU, distance and angle were fused using logistic regression. The performance evaluation in the sensor fusion scenario has shown an effective 5% improvement in object detection performance compared to the usage of single sensor.