• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.247-254
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• 2011

This paper presents a method of sonar grid map matching for topological place recognition. The proposed method provides an effective rotation invariant grid map matching method. A template grid map is firstly extracted for reliable grid map matching by filtering noisy data in local grid map. Using the template grid map, the rotation invariant grid map matching is performed by Ring Projection Transformation. The rotation invariant grid map matching selects candidate locations which are regarded as representative point for each node. Then, the topological place recognition is achieved by calculating matching probability based on the candidate location. The matching probability is acquired by using both rotation invariant grid map matching and the matching of distance and angle vectors. The proposed method can provide a successful matching even under rotation changes between grid maps. Moreover, the matching probability gives a reliable result for topological place recognition. The performance of the proposed method is verified by experimental results in a real home environment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.2
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• pp.97-103
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• 2005

Most car navigation systems(CNS) estimate the vehicle's location using global positioning system(GPS) or dead reckoning(DR) system. However, the estimated location has undesirable errors because of various noise sources such as unpredictable GPS noises. As a result, the measured position is not lying on the road, although the vehicle is known to be restricted on the road network. The purpose of map matching is to locate the vehicle's position on the road network where the vehicle is most likely to be positioned. In this paper, we analyze some general map matching algorithms first. Then, we propose a map matching method using compensation vectors to improve the performance of map matching. The proposed method calculates a compensation vector from the discrepancy between a measured position and an estimated position. The compensation vector and a newly measured position are to be used to determine the next estimation. To show the performance improvement of the map matching using compensation vectors, the real time map matching experiments are performed. The real road experiments demonstrate the effectiveness and applicability of the proposed map matching.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.23-25
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• 2010

In this paper, we studied linking method for updating navigation map with new address map. This method is hierarchical as we chose candidate using attribute data for geometry matching. Limiting a matching range, we conducted a experiment ICP with geometry matching. As a result, for linking method it's quite satisfied but for updating method, we decided that we have to do more precise method for updating.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.4 s.304
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• pp.1-10
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• 2005

This paper presents a map matching method using multiple hypothesis technique(MHT) to identify the road that a land vehicle is located on. To realize a map matching method using MHT, Pseudo-measurements are generated utilizing adjacent roads of GPS/DR position and the MHT is reformulated as a single target problem. Since pseudo-measurements are generated using digital map, topological properties such as road connection, direction, and road facility information are considered in calculating probabilities of hypotheses. In order to improve the map matching performance under when bias errors exist in digital road map data, a Kalman filter is employed to estimate the biases. Field experimental results show that the proposed map matching method provides the consistent performance even in complex downtown areas, overpass/underpass areas, and in the areas where roads are adjacent in parallel.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.92.3-92
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• 2002

A lot of localization algorithms have been developed in order to achieve autonomous navigation. However, most of localization algorithms are restricted to certain conditions. In this paper, Monte Carlo localization scheme with a map-matching algorithm is suggested as a robust localization method for the Public Service Robot to accomplish its tasks autonomously. Monte Carlo localization can be applied to local, global and kidnapping localization problems. A range image based measure function and a geometric pattern matching measure function are applied for map matching algorithm. This map matching method can be applied to both polygonal environments and un-polygonal environments and achieves...

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.9
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• pp.916-921
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• 2011

Mobile robot localization is the task of estimating the robot pose in a given environment. This research deals with outdoor localization based on an elevation map. Since outdoor environments are large and contain many complex objects, it is difficult to robustly estimate the robot pose. This paper proposes a Hausdorff distance-based map matching method. The Hausdorff distance is exploited to measure the similarity between extracted features obtained from the robot and elevation map. The experiments and simulations show that the proposed Hausdorff distance-based map matching is useful for robust outdoor localization using an elevation map. Also, it can be easily applied to other probabilistic approaches such as a Markov localization method.

• Journal of the Semiconductor & Display Technology
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• v.15 no.4
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• pp.103-106
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• 2016

3D technology is main factor in Augmented Reality. Depth map is essential to make cubic effect using 2d image. There are a lot of ways to construct Depth map. Among them, stereo matching is mainly used. This paper presents how to generate depth map using stereo matching. For stereo matching, existing Dynamic programming method is used. To make accurate stereo matching, High-Boost Filter is applied to preprocessing method. As a result, when depth map is generated, accuracy based on Ground Truth soared.

• Korean Journal of Remote Sensing
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• v.35 no.6_1
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• pp.907-917
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• 2019

Recently, the demand for high-resolution satellite images increases in many fields such as land monitoring and terrain analysis. Therefore, the need for geometric correction is increasing. As an automatic precision geometric correction method, there is a method of automatically extracting the GCP by matching between the GCP Chip and the satellite image. For automatic precision geometric correction, the success rate of matching GCP Chip and satellite image is important. Therefore, it is important to evaluate the matching performance of the manufactured GCP Chip. In order to evaluate the matching performance of GCP Chips, a total of 3,812 GCP Chips in South Korea were used as experimental data. The GCP Chip matching results of KOMPSAT-3A and Google Map showed similar matching results. Therefore, we determined that Google Map satellite imagery could replace high-resolution satellite imagery. Also, presented a method using center point and error radius of Google Map to reduce the time required to verify matching performance. As a result, it is best to set the optimum error radius to 8.5m. Evaluated the matching performance of GCP Chips in South Korea using Google Maps. And verified matching result using presented method. As a result, the GCP Chip s in South Korea had a matching success rate of about 94%. Also, the main matching failure factors were analyzed by matching failure GCP Chips. As a result, Except for GCP Chips that need to be remanufactured, the remaining GCP Chips can be used for the automatic geometric correction of satellite images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.418-422
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• 2009

Generally, algorithms for generating disparity maps can be clssified into two categories: region-based method and feature-based method. The main focus of this research is to generate a disparity map with an accuracy depth information for 3-dimensional reconstructing. Basically, the region-based method and the feature-based method are simultaneously included in the proposed algorithm, so that the existing problems including false matching and occlusion can be effectively solved. As a region-based method, regions of false matching are extracted by the proposed MMAD(Modified Mean of Absolute Differences) algorithm which is a modification of the existing MAD(Mean of Absolute Differences) algorithm. As a feature-based method, the proposed method eliminates false matching errors by calculating the vector with SIFT and compensates the occluded regions by using a pair of adjacent SIFT matching points, so that the errors are reduced and the disparity map becomes more accurate.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.38.4-38
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• 2002

We propose a fast map-matching algorithm based on the length and the slope for the sequence of lines extracted from a laser range finder and a map. After finding two feature set from laser data and a map, the position and heading of the mobile robot can be determined exactly.