• Journal of Sensor Science and Technology
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• v.28 no.5
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• pp.334-339
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• 2019

In this study, we propose a compensation method of raw LiDAR data with noise and noise filtering for signal processing of LiDAR sensors during the development phase. The raw LiDAR data include constant errors generated by delays in transmitting and receiving signals, which can be resolved by LiDAR signal compensation. The signal compensation consists of two stage. First one is LiDAR sensor calibration for a compensation of geometric distortion. Second is walk error compensation. LiDAR data also include fluctuation and outlier noise, the latter of which is removed by data filtering. In this study, we compensate for the fluctuation by using the Kalman filter method, and we remove the outlier noise by applying a Gaussian weight function.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.977-985
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• 2021

The LiDAR sensor is attracting attention as a key sensor for autonomous driving vehicle. LiDAR sensor provides measured three-dimensional lengths within range using LASER. However, as much data is provided to the external system, it is difficult to process such data in an external system or processor of the vehicle. To resolve these issues, we develop integrated processing system for LiDAR sensor. The system is configured that client receives data from LiDAR sensor and processes data, server gathers data from clients and transmits integrated data in real-time. The test was carried out to ensure real-time processing of the system by changing the data acquisition, processing method and process driving method of process. As a result of the experiment, when receiving data from four LiDAR sensors, client and server process was operated using background or multi-core processing, the system response time of each client was about 13.2 ms and the server was about 12.6 ms.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.345-352
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• 2022

Recent LiDAR(Light Detection And Ranging) sensor is used for scanning object around in real-time. This sensor can detect movement of the object and how it has changed. As the production cost of the sensors has been decreased, LiDAR begins to be used for various industries such as facility guard, smart city and self-driving car. However, LiDAR has a large input data size due to its real-time scanning process. So another way for processing a large amount of data are needed in LiDAR system because it can cause a bottleneck. This paper proposes edge computing to compress massive point cloud for processing quickly. Since laser's reflection range of LiDAR sensor is limited, multiple LiDAR should be used to scan a large area. In this reason multiple LiDAR sensor's data should be processed at once to detect or recognize object in real-time. Edge computer compress point cloud efficiently to accelerate data processing and decompress every data in the main cloud in real-time. In this way user can control LiDAR sensor in the main system without any bottleneck. The system we suggest solves the bottleneck which was problem on the cloud based method by applying edge computing service.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.215-225
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• 2020

Light detection and ranging (LiDAR) sensors detect the distance of the surrounding environment and objects. Conventional LiDAR sensors require a certain amount of a power because they detect objects by transmitting lasers at a regular interval depending on a constant resolution. The constant power consumption from operating multiple LiDAR sensors is detrimental to autonomous and electric vehicles using battery power. In this paper, we propose two algorithms that improve the inefficient power consumption during the constant operation of LiDAR sensors. LiDAR sensors with algorithms efficiently reduce the power consumption in two ways: (a) controlling the resolution to vary the laser transmission period (T_P) of a laser diode (LD) depending on the vehicle's speed and (b) reducing the static power consumption using a sleep mode depending on the surrounding environment. A proposed LiDAR sensor with a resolution control algorithm reduces the power consumption of the LD by 6.92% to 32.43% depending on the vehicle's speed, compared to the maximum number of laser transmissions (N_x·max). The sleep mode with a surrounding environment-sensing algorithm reduces the power consumption by 61.09%. The proposed LiDAR sensor has a risk factor for 4-cycles that does not detect objects in the sleep mode, but we consider it to be negligible because it immediately switches to an active mode when a change in surrounding conditions occurs. The proposed LiDAR sensor was tested on a commercial processor chip with the algorithm controlling the resolution according to the vehicle's speed and the surrounding environment.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.157-163
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• 2019

The LiDAR sensor, which is widely regarded as one of the most important sensors, has recently undergone active commercialization owing to the significant growth in the production of ADAS and autonomous vehicle components. The LiDAR sensor technology involves radiating a laser beam at a particular angle and acquiring a three-dimensional image by measuring the lapsed time of the laser beam that has returned after being reflected. The LiDAR sensor has been incorporated and utilized in various devices such as drones and robots. This study focuses on object detection and recognition by employing sensor fusion. Object detection and recognition can be executed as a single function by incorporating sensors capable of recognition, such as image sensors, optical sensors, and propagation sensors. However, a single sensor has limitations with respect to object detection and recognition, and such limitations can be overcome by employing multiple sensors. In this paper, the performance of an eight-channel scanning LiDAR was evaluated and an object detection algorithm based on it was implemented. Furthermore, object detection characteristics during daytime and nighttime in a real road environment were verified. Obtained experimental results corroborate that an excellent detection performance of 92.87% can be achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1859-1867
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• 2021

Multi LiDAR sensors are being mounted on autonomous vehicles, and a system to multi LiDAR sensors data is required. When sensors data is transmitted or processed to the main processor, a huge amount of data causes a load on the transport network or data processing. In order to minimize the number of load overhead into LiDAR sensor processors, only semantic data is transmitted through data comparison between frames in LiDAR data. When data from 4 LiDAR sensors are processed in a static environment without moving objects and a dynamic environment in which a person moves within sensor's field of view, in a static experiment environment, the transmitted data reduced by 89.5% from 232,104 to 26,110 bytes. In dynamic environment, it was possible to reduce the transmitted data by 88.1% to 29,179 bytes.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.244-245
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• 2009

일반적으로 LiDAR(Light Detection And Ranging)의 자료로부터 3차원 위치정보와 속성 정보를 취득하여 활용 하는 연구가 많이 진행되고 있다. 본 연구에서는 Grid($100m{\times}100m$) 기반인 2차원적 Grid Point를 통해 Sensor Field를 정하고 LiDAR의 3차원적 좌표정보를 이용하여 최적 센서 위치를 선정하고 중간에 장애물(Obstacle)이 존재하는 경우 또한 알고리즘을 통해 최적위치인 Grid point를 선정하였다. 알고리즘은 3가지 측면을 고려하여 분류하였다. 첫째 장애물이 없는(Non Obstacle) 2차원적인 경우, 둘째 장애물이 존재(Obstacle)하는 2차원적인 경우, 셋째 장애물이 존재(Obstacle)하며 3차원적인 알고리즘을 고려하였다. 향후 연구에서는 LiDAR를 직접 적용하여 최적 선정 지역을 도출하여 알고리즘을 적용할 것이다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.1-6
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• 2020

UAV (Unmanned Aerial Vehicles) are used widely for military purposes because they are more economical than general manned aircraft and satellites, and have easy access to the object. Recently, owing to the development of IT technology, UAV equipped with various sensors have been released, and their use is increasing in a wide range of fields, such as surveying, agriculture, meteorological observation, communication, broadcasting, and sports. An increasing number of studies and attempts have made use of it. On the other hand, existing research was related mostly to photogrammetry, but there has been a lack of analytical research on LiDAR (Light Detection And Ranging). Therefore, this study examined the characteristics of a UAV LiDAR sensor for the application of a geospatial information field. In this study, the performance of commercialized LiDAR sensors, such as the acquisition speed and the number of echoes, was investigated, and data acquisition and analysis were conducted by selecting Surveyor Ultra and VX15 models with similar accuracy and data acquisition distances. As a result, a DSM of each study site was generated for each sensor, and the characteristics of data density, precision, and acquisition of ground data from vegetation areas were presented through comparison. In addition, the UAV LiDAR sensor showed an accuracy of 0.03m ~ 0.05m. Hence, it is necessary to select equipment considering the characteristics of data for effective use. In the future, the use of UAV LiDAR may be suggested if additional data can be obtained and analyzed for various areas, such as urban areas and forest areas.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.281-284
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• 2010

Recently the development of digital based measurement sensor with which a variety of surveying equipment and methods are being developed. In the field of aerial mapping using GPS, INS and Digital Camera instead of Film based Camera. In case of aerial photogrammetry for mapping, it is effective on wide area. But it is ineffective on narrow area. Therefore, the research experimented that used LiDAR sensor based mobile mapping systems for modify and update about region of Suwon and Yeouido. From these results a possibility and an effectiveness analyzed and evaluated LiDAR sensor based mobile mapping systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1416-1426
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• 2021

In the case of a scanning type multi-channel LiDAR sensor, the distance error called a walk error may occur due to a difference in received signal power. This work error causes different distance values to be output for the same object when scanning the surrounding environment based on multiple LiDAR sensors. For minimizing walk error in overlapping regions when scanning all directions using multiple sensors, to calibrate distance for each channels using convolution on external system. Four sensors were placed in the center of 6×6 m environment and scanned around. As a result of applying the proposed filtering method, the distance error could be improved by about 68% from average of 0.5125 m to 0.16 m, and the standard deviation could be improved by about 48% from average of 0.0591 to 0.030675.