• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1053-1060
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• 2016

As location recognition for autonomous vehicles develops, the need for a precise map for autonomous driving has increased. A precise map must be built based upon accurate position. Recent studies have accelerated research in this area by using various sensors that calculate the accurate position by comparing and recognizing objects around the roads. However, application of such methods is limited because these studies only take objects with significant verticality into consideration. Thus, new research is needed to overcome the limitations: a method that is not constrained by the existence of certain types of surrounding objects shall be proposed. Most roads contain road marking information, such as lanes, direction signs, and pedestrian crossings. Such information on the road surface is a valuable resource for building a precise map. This paper proposes a method of building a precise map by using road marking information.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1184-1189
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• 2003

Map building and position estimation capabilities are practically indispensable for a mobile robot to execute its given tasks in its working environments. An autonomous map building method and a smart localization method is proposed in our previous works. The experimental verifications are carried out in this paper. We applied the proposed algorithms to mobile service robots in large-scale indoor buildings. Experimental results show that our strategy is reliable and feasible in tough conditions like non-polygonal and dynamic environments. The advantages of the algorithms are well-illustrated through real experiments.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.2
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• pp.351-358
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• 2008

This paper introduces a map building algorithm which can collect environmental information using ultrasonic sensors. And also this paper discusses a traveling algorithm using environmental information which leads to the map building algorithm. In order to accomplish the proposed traveling algorithm, this paper additionally discusses a path revision algorithm. For verifying the proposed algorithms, several experiments are executed using a mobile robot physically designed in this paper. The conclusion is that the proposed algorithm is very effective and is applicable to mobile robots especially requiring a low-cost environmental information.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.724-728
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• 2005

This paper represents a development of a range finder sensor module for indoor 2-D mapping and modified Hough transformation for map building. A range finder sensor module has been developed by using optic PSD (Position Sensitive Detector) sensor array at a low price. While PSD sensor is cost effective and light weighting, it has switching noise and white noise. To remove these noises, we propose a heuristic filter. For line-based map building, also we proposed advanced Hough transformation and navigation algorithm. Some experiments were illustrated for the validity of the developed system.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.102-107
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• 2011

In this paper we present cooperation of heterogeneous robot team, composed of a wheeled robot and a helicopter for localization and map building. This heterogeneous robot team can successfully fulfill task by combining the abilities of both robots than single robot because wheeled robot and helicopter have complementing ability. The scenario describes a tightly cooperative task, where the wheeled robot move carrying the helicopter and detect obstacles, if there are obstacles, helicopter take off for map building and land, then robot team move destination avoiding obstacles. We present PID controller for position control of helicopter and transformation algorithm to global coordinate from image pixel coordinate. Experimental result show that the proposed method is valid.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.1
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• pp.184-191
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• 2008

In order for a mobile robot to move under unknown or uncertain environment, it must have an environmental information. In collecting environmental information, the mobile robot can use various sensors. In case of using ultrasonic sensors to collect an environmental information, it is able to comprise a low-cost environmental recognition system compared with using other sensors such as vision and laser range-finder. This paper proposes a map building algorithm which can collect environmental information using ultrasonic sensors. And also this paper suggests a traveling algorithm using environmental information which leads to the map building algorithm. In order to accomplish the proposed traveling algorithm, this paper additionally discusses a position revision algorithm.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.281-285
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• 2009

Mobile robot has various sensors for describing the external world. The ultrasonic sensor widely applied to the most mobile robot to detect the obstacle and environment owing to low cost, its easy to use. However, ultrasonic sensor has major problems: the uncertainty information of sensor, false readings caused by specular reflection, multi path effect, low angular resolution and sensitivity to changes in temperature and humidity. This paper describes a sensor system for map building of mobile robot. It was made of low cost PSD (Position Sensitive Detector) sensor array and high speed RISC MPU. PSD sensor is cost effective and light weighting but its output signal has many noises. We propose heuristic S/W filter to effectively remove these noises. The developed map building sensor system was equipped on a mobile robot and was compared with ultrasonic sensor through field test.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.256-262
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• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.

• The Journal of Korea Robotics Society
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• v.4 no.3
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• pp.169-176
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• 2009

Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot's orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot's pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.7-18
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• 2005

A key component of an autonomous mobile robot is to localize itself accurately and build a map of the environment simultaneously. In this paper, we propose a vision-based mobile robot localization and mapping algorithm using scale-invariant features. A camera with fisheye lens facing toward to ceiling is attached to the robot to acquire high-level features with scale invariance. These features are used in map building and localization process. As pre-processing, input images from fisheye lens are calibrated to remove radial distortion then labeling and convex hull techniques are used to segment ceiling region from wall region. At initial map building process, features are calculated for segmented regions and stored in map database. Features are continuously calculated from sequential input images and matched against existing map until map building process is finished. If features are not matched, they are added to the existing map. Localization is done simultaneously with feature matching at map building process. Localization. is performed when features are matched with existing map and map building database is updated at same time. The proposed method can perform a map building in 2 minutes on $50m^2$ area. The positioning accuracy is ${\pm}13cm$, the average error on robot angle with the positioning is ${\pm}3$ degree.