• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.28-35
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• 2009

Positioning technology which a part technology of Mobile Robot is an essential technology to locate the position of Robot and navigate to wanted position. The Robot that based on wheel drive uses Odometry position. technology. But when using Odometry positioning technology, it's hard to find out constant error value because a slip phenomenon occurs as the Robot runs. In this paper, we present the way to minimize positioning error by using Odometry and Inertial sensor. Also, the way to reduce error with Inertial sensor on SLAM using image will be shown, too.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.179-188
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• 2021

Construction monitoring is one of the key modules in smart construction. Unlike structured urban environment, construction site mapping is challenging due to the characteristics of an unstructured environment. For example, irregular feature points and matching prohibit creating a map for management. To tackle this issue, we propose a system for data acquisition in unstructured environment and a framework for Intensity and Ambient Enhanced Lidar Inertial Odometry via Smoothing and Mapping, IA-LIO-SAM, that achieves highly accurate robot trajectories and mapping. IA-LIO-SAM utilizes a factor graph same as Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping (LIO-SAM). Enhancing the existing LIO-SAM, IA-LIO-SAM leverages point's intensity and ambient value to remove unnecessary feature points. These additional values also perform as a new factor of the K-Nearest Neighbor algorithm (KNN), allowing accurate comparisons between stored points and scanned points. The performance was verified in three different environments and compared with LIO-SAM.

• Smart Structures and Systems
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• v.31 no.6
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• pp.561-571
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• 2023

Ensuring the safety of a structure necessitates that repairs are carried out based on accurate inspections and records of damage information. Traditional methods of recording damage rely on individual paper-based documents, making it challenging for inspectors to accurately record damage locations and track chronological changes. Recent research has suggested the adoption of building information modeling (BIM) to record detailed damage information; however, localizing damages on a BIM model can be time-consuming. To overcome this limitation, this study proposes a method to automatically localize damages on a BIM model in real-time, utilizing consecutive images and measurements from an inertial measurement unit in close proximity to damages. The proposed method employs a visual-inertial odometry algorithm to estimate the camera pose, detect damages, and compute the damage location in the coordinate of a prebuilt BIM model. The feasibility and effectiveness of the proposed method were validated through an experiment conducted on a campus building. Results revealed that the proposed method successfully localized damages on the BIM model in real-time, with a root mean square error of 6.6 cm.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.91-92
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• 2008

Localization and its applications are very important area of the mobile robot technology. Especially, accurate localization is needed when we move the mobile robot to the goal position. In indoor cases, Global Positioning System(GPS) is not suitable but Radio Frequency Identification(RFID) technology can provide position data to the robot. A proposed algorithm in this paper uses not only odometry data but also RFID data to improve estimation of true position of the robot with the particle filtering.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.666-669
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• 2000

Localization is the process of aligning the robot's local coordinates with the global coordinates of a map. A mobile robot's location is basically computed by a dead reckoning scheme, but this position information becomes increasingly inaccurate during navigation due to odometry errors. In this paper, the method of building a map of a robot's environment using ultrasonic sensor data and the occupancy grid map scheme is briefly presented. Then, the search and matching algorithms to compensate for the odometry error by comparing the local map with the reference map are proposed and verified by experiments. It is shown that the compensated error is not accumulated and exists within the limited range.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1807-1808
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• 2008

When it is used for autonomous mobile robots by using dead-reckoning system, odometry system with encorder is the simplest method as well as well-known in the industry. However, odometry system is reflected slide, friction and mechanical errors of wheels when operating the position estimation. And also in order to minimize errors of direction angle which is the most important factor that it is designed the controller in controlling kinematics and quadratic curve, PID that came into the values of sensor fusion with encorder and gyroscope sensor. After designing, the autonomous mobile robot is producted practically and inspected how it works.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.678-687
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• 2005

This paper describes a relative localization algorithm using odometry data and consecutive local maps. The purpose of this paper is the odometry error correction using the area matching of two consecutive local maps. The local map is built up using a sensor module with dual laser beams and USB camera. The range data form the sensor module is measured using the structured lighting technique (active stereo method). The advantage in using the sensor module is to be able to get a local map at once within the camera view angle. With this advantage, we propose the AVS (Aligned View Sector) matching algorithm for. correction of the pose error (translational and rotational error). In order to evaluate the proposed algorithm, experiments are performed in real environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.686-691
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• 2008

This paper proposes the concept of "virtual sensor data" and its application for real time obstacle avoidance. The virtual sensor data is virtual distance which takes care of the movement of the obstacle as well as that of the robot. In practical application, the virtual sensor data is calculated from the odometry data and the range sensor data. The virtual sensor data can be used in all the methods which use distance data for collision avoidance. Since the virtual sensor data considers the movement of the robot and the obstacle, the methods utilizing the virtual sensor data results in more smooth and safer collision-free motion.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.114-121
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• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

• The Journal of Korea Robotics Society
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• v.17 no.2
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• pp.183-190
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• 2022

Accurate kinematic parameters of mobile robots are essential because inaccurate kinematic model produces considerable uncertainties on its odometry and control. Especially, kinematic parameters of caster type mobile robots are important due to their complex kinematic model. Despite the importance of accurate kinematic parameters for caster type mobile robots, few research dealt with the calibration of the kinematic model. Previous study proposed a calibration method that can only calibrate double-wheeled caster type mobile robot and requires direct-measuring of robot center point and distance between casters. This paper proposes a calibration method based on geometric approach that can calibrate single-wheeled caster type mobile robot with two or more casters, does not require direct-measuring, and can successfully acquire all kinematic parameters required for control and odometry. Simulation and hardware experiments conducted in this paper validates the proposed calibration method and shows its performance.