• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.207-218
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• 2006

The extraction of horizontal alignment and cross-section of roads is very important task in road safety diagnosis. Existing road safety diagnosis methods by investigators need much time and expense but don't provide various data. Therefor, we need road shape classification automatically and extraction method of horizontal alignment and cross-section of roads through digital photogrammetry system using GPS-VAN with laser scanner. In this paper, we propose a method of mobile laser scanning data acquisition, processing and developing extraction methods of horizontal alignment and cross-section of roads using mobile laser scanning data by GPS-VAN.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.1
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• pp.43-62
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• 2017

Mobile pulse scanning Light Detection And Ranging (LIDAR) are essential components of intelligent vehicles capable of autonomous travel. Obstacle detection functions of autonomous vehicles require very low failure rates. With the increasing number of autonomous vehicles equipped with scanning LIDARs to detect and avoid obstacles and navigate safely through the environment, the probability of mutual interference becomes an important issue. The reception of foreign laser pulses can lead to problems such as ghost targets or a reduced signal-to-noise ratio. This paper will show the probability that any two scanning LIDARs will interfere mutually by considering spatial and temporal overlaps. We have conducted four experiments to investigate the occurrence of the mutual interference between scanning LIDARs. These four experimental results introduced the effects of mutual interference and indicated that the interference has spatial and temporal locality. It is hard to ignore consecutive mutual interference on the same line or the same angle because it is possible the real object not noise or error. It may make serious faults because the obstacle detection functions of autonomous vehicle rely on heavily the scanning LIDAR.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.105-110
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• 1999

This paper presents a local obstacle avoidance method for indoor mobile robots using Lane method and velocity Space Command approach. The method locates local obstacles using the information form multi-sensors, such that ultrasonic sensor array and laser scanning sensor. The method uses lane method to determine optimum collision-free heading direction of a robot. Also, it deals with the robot motion dynamics problem to reduce some vibration and guarantee fast movement as well. It yields translational and rotational velocities required to avoid the detected obstacles and to keep the robot heading direction toward goal location as close as possible. For experimental verification of the method, a mobile robot driven by two AC servo motors, equipped with 24 ultrasonic sensor array and laser scanning sensor navigates using the method through a corridor cluttered with obstacle.

• Korean Journal of Optics and Photonics
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• v.7 no.3
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• pp.191-195
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• 1996

A optical system of a movile LIDAR is designed for air pollution research. After the inverse Cassegrain type collimator, the laser beam falls on the mirror which serve for coinciding optical axis of laser beam and the receiving telescope. Then, it is directed into the atmosphere and back scattered radiation back to the receiving telescope by the scanning mirror. The unit of scanning mirror allows to rotate the mirror along the altitude 0$^{\circ}$~60$^{\circ}$, and the azimuth 0$^{\circ}$~360$^{\circ}$. The scanning mirror is not connected with the receiving telescope but placed on the roof of the mobile. The received beam is spatial filtered by a spatial filter and collimated by a fabric lens. Thereafter, the beam is devided into 2 channel for registration by a beam splitter. Each laser beam is transformed into an electrical signal by means of the photomultifier and then processed to be analyzed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.458-464
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• 2015

This paper proposes a portable 3-D scanning technique using a laser pointer. 3-D scanning is a process that acquires surface information from an 3-D object. There have been many studies on 3-D scanning. The methods of 3-D scanning are summarized into some methods based on multiple cameras, line lasers, and light pattern recognition. However, those methods has major disadvantages of their high cost and big size for portable appliances such as smartphones and digital cameras. In this paper, a 3-D scanning system using a low-cost and small-sized laser pointer are introduced to solve the problems. To do so, we propose a 3-D localization technique for a laser point. The proposed method consists of two main parts; one is a fast recognition of input images to obtain 2-D information of a point laser and the other is calibration based on the least-squares technique to calculate the 3-D information overall. To verified our method, we carry out experiments. It is proved that the proposed method provides 3-D surface information although the system is constructed by extremely low-cost parts such a chip laser pointer, compared to existing methods. Also, the method can be implemented in small-size; thus, it is enough to use in mobile devices such as smartphones.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.443-449
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• 2018

Acquiring indoor point cloud, using SLAM (Simultaneous Localization and Mapping) based mobile mapping system, is an element progress for development of as-build BIM (Building Information Model) for the maintenance of the building. In this research we proposed a simulation-based target geometry determination for extrinsic calibration of multiple 2D laser scanning mobile system. Four different types of calibration sites were designed: (1) circle type; (2) rectangle type; (3) double circle type; and (4) double rectangle type. Based on the measurement values obtained from each simulated calibration site geometry, least squares solution based extrinsic calibration was derived. As a result, the rectangle type geometry is most suitable for extrinsic calibration of this system. Also, correlation values between extrinsic calibration parameters were high, and calibration results were distinct according to the calibration sites.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.776-787
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• 2002

Automation of welding process in shipyards is ultimately necessary, since the welding site is spatially enclosed by floors and girders, and therefore welding operators are exposed to hostile working conditions. To solve this problem, a welding mobile robot that can navigate autonomously within the enclosure has been developed. To achieve the welding task in the closed space, the robotic welding system needs a sensor system for the working environment recognition and the weld seam tracking, and a specially designed environment recognition strategy. In this paper, a three-dimensional laser vision system is developed based on the optical triangulation technology in order to provide robots with 3D work environmental map. Using this sensor system, a spatial filter based on neural network technology is designed for extracting the center of laser stripe, and evaluated in various situations. An environment modeling algorithm structure is proposed and tested, which is composed of the laser scanning module for 3D voxel modeling and the plane reconstruction module for mobile robot localization. Finally, an environmental recognition strategy for welding mobile robot is developed in order to recognize the work environments efficiently. The design of the sensor system, the algorithm for sensing the partially structured environment with plane segments, and the recognition strategy and tactics for sensing the work environment are described and discussed with a series of experiments in detail.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.1
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• pp.1-8
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• 2012

Obstacle detection is much studied by using sensors such as laser, vision, radar and ultrasonic in path planning for UGV(Unmanned Ground Vehicle), but not much reported about its characterization. In this paper not only an obstacle classification method using 2-dimensional LMS(Laser Measurement System) but also a decision making method whether to avoid or traverse the obstacle is proposed. The basic idea of decision making is to classify the characteristics by 2D laser scanned data and intensity data. Roughness features are obtained by range data using a simple linear regression model. The standard deviations of roughness and intensity data are used as measures for decision making by comparing with those of reference data. The obstacle classification and decision making for the UGV can facilitate a short path to the target position and the survivability of the robot.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.195-208
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• 2023

This study aimed to use three-dimensional point cloud data (PCD) obtained from Terrestrial Laser Scanning (TLS) and Mobile Laser Scanning (MLS) to evaluate a deep learning-based species classification model for two tree species: Pinus koraiensis and Larix kaempferi. Sixteen models were constructed based on the three conditions: LiDAR platform (TLS and MLS), down-sampling intensity (1024, 2048, 4096, 8192), and deep learning model (PointNet, PointNet++). According to the classification accuracy evaluation, the highest kappa coefficients were 93.7% for TLS and 96.9% for MLS when applied to PCD data from the PointNet++ model, with down-sampling intensities of 8192 and 2048, respectively. Furthermore, PointNet++ was consistently more accurate than PointNet in all scenarios sharing the same platform and down-sampling intensity. Misclassification occurred among individuals of different species with structurally similar characteristics, among individual trees that exhibited eccentric growth due to their location on slopes or around trails, and among some individual trees in which the crown was vertically divided during tree segmentation.