• Journal of Institute of Control, Robotics and Systems
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• v.17 no.2
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• pp.164-170
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• 2011

This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) method using both position and orientation information of ceiling lamps. Conventional approaches used corner or line features as landmarks in their SLAM algorithms, but these methods were often unable to achieve stable navigation due to a lack of reliable visual features on the ceiling. Since lamp features are usually placed some distances from each other in indoor environments, they can be robustly detected and used as reliable landmarks. We used both the position and orientation of a lamp feature to accurately estimate the robot pose. Its orientation is obtained by calculating the principal axis from the pixel distribution of the lamp area. Both corner and lamp features are used as landmarks in the EKF (Extended Kalman Filter) to increase the stability of the SLAM process. Experimental results show that the proposed scheme works successfully in various indoor environments.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.353-364
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• 2009

In this paper, a SLAM (Simultaneous Localization and Mapping) method using a single camera and planar fiducial markers is proposed. Fiducial markers are planar patterns that are mounted on the ceiling or wall. Each fiducial marker has a unique hi-tonal identification pattern with square outlines. It can be printed on paper to reduce cost or it can be painted using retro-reflective paint in order to make invisible and prevent undesirable visual effects. Existing localization methods using artificial landmarks have the disadvantage that landmark locations must be known a priori. In contrast, the proposed method can build a map and estimate robot location even if landmark locations are not known a priori. Hence, it reduces installation time and setup cost. The proposed method works good even when only one fiducial marker is seen at a scene. We perform computer simulation to evaluate proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.5
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• pp.479-486
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• 2012

In this paper, a ranging system is proposed that is able to measure 360 degree omnidirectional distances to environment objects. The ranging system is based on the structured light imaging system with catadioptric omnidirectional mirror. In order to make the ranging system robust against environmental illumination, efficient structured light image processing algorithms are developed; sequential integration of difference images with modulated structured light and radial search based on Bresenham line drawing algorithm. A dedicated FPGA image processor is developed to speed up the overall image processing. Also the distance equation is derived in the omnidirectional imaging system with a hyperbolic mirror. It is expected that the omnidirectional ranging system is useful for mapping and localization of mobile robot. Experiments are carried out to verify the performance of the proposed ranging system.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1234-1239
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• 2011

The scan matching is widely used in localization and mapping of mobile robots. This paper presents a probabilistic scan matching method. To improve the performance of the scan matching, a direction of data point is incorporated into the scan matching. The direction of data point is calculated using the line fitted by the neighborhood data. Owing to the incorporation, the performance of the matching was improved. The number of iterations in the scan matching decreased, and the tolerance against a high rotation between scans increased. Based on real data of a laser range finder, experiments verified the performance of the proposed direction augmented probabilistic scan matching algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.40-47
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• 2010

This paper presents a simultaneous localization and mapping (SLAM) of an indoor hallway environment using Rao-Blackwellized particle filter (RBPF) along with a line segment as a landmark. Based on the fact that fluent line features can be extracted around the ceiling and side walls of hallway using vision sensor, a horizontal line segment is extracted from an edge image using Hough transform and is also tracked continuously by an optical flow method. A successive observation of a line segment gives initial state of the line in 3D space. For data association, registered feature and observed feature are matched in image space through a degree of overlap, an orientation of line, and a distance between two lines. Experiments show that a compact environmental map can be constructed with small number of horizontal line features in real-time.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.218-224
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• 2009

This paper proposes an effective SLAM based on redundant extended Kalman filter for robot navigation in an irregular surface to enhance the accuracy of robot's pose. To establish an accurate model of a caterpillar type robot is very difficult due to the mechanical complexity of the system which results in highly nonlinear behavior. In addition, for robot navigation on an irregular surface, its control suffers from the uncertain pose of the robot heading closely related to the condition of the floor. We show how this problem can be overcome by the proposed approach based on redundant extended Kalman filter through some computer simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.858-862
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• 2015

This paper presents a new forward-viewing mono-camera based obstacle detection algorithm for mobile robots. The proposed method extracts the coarse location of an obstacle in an image using inverse perspective mapping technique from sequential images. In the next step, graph-cut based image labeling is conducted for estimating the exact obstacle boundary. The graph-cut based labeling algorithm labels the image pixels as either obstacle or floor as the final outcome. Experiments are performed to verify the obstacle detection performance of the developed algorithm in several examples, including a book, box, towel, and flower pot. The low illumination condition, low color contrast between floor and obstacle, and floor pattern cases are also tested.

• Journal of Korea Multimedia Society
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• v.8 no.5
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• pp.721-728
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• 2005

A virtual machine is a conceptual computer with a logical system configuration, made of software unlike physical systems made of hardware. Virtual machine technology for embedded systems is a requisite software technology for download solutions such as mobile devices, digital TVs, etc. At present, a research of virtual machines for embedded systems named EVM(Embedded Virtual Machine) is in progress. As a part of the research, we define the EFF(Executable File Format) as a file format for embedded systems. We also prove completeness of EFF by structurally mapping class file widely used to EFF.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.112-121
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• 2016

ADAS(Advanced Driver Assistance Systems) and street furniture information collecting car like as MMS(Mobile Mapping System), they require object location estimation method for recognizing spatial information of object in road images. But, the case of conventional methods, these methods require additional hardware module for gathering spatial information of object and have high computational complexity. In this paper, for a coordinate of road sign in single camera image, a position estimation scheme of object in road images is proposed using the relationship between the pixel and object size in real world. In this scheme, coordinate value and direction are used to get coordinate value of a road sign in images after estimating the equation related on pixel and real size of road sign. By experiments with test video set, it is confirmed that proposed method has high accuracy for mapping estimated object coordinate into commercial map. Therefore, proposed method can be used for MMS in commercial region.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.