• 로봇학회논문지
• /
• 제6권3호
• /
• pp.247-254
• /
• 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.

• 한국주거학회논문집
• /
• 제23권5호
• /
• pp.19-29
• /
• 2012

It is very necessary to know about child recognition of residential environment to plan and design an environment proper for children's growth and development. The research method using Cognitive Map, which may be defined as "an overall mental image of representation of the space and layout of a setting" can be a good tool for studying child recognition of residential environment. This study analyzed the child recognition of the size of home range, the number of residential environment elements, the types of Cognitive Map and the levels of Cognitive Map to understand the contents of child recognition about their residential environment. Subjects were 206 children in age6, 8 and 10 in Gwanju and Jeonnam area. As the result of the study, we found that 70% of child recognized 100~500 M as the size of home range, and that the number of the elements of residential environment was 7, average. And we also found that sequential map was more popular than spatial map in child's Cognitive Map type and that almost 60% of child respondents drew the Cognitive Map of level 1 complexity type. As the result of this study, we could know that the research method using Cognitive Map was very useful for understanding the child recognition of residential environment.

• 제어로봇시스템학회논문지
• /
• 제17권1호
• /
• pp.47-53
• /
• 2011

To enable the efficient operation of ITS, it is necessary to collect location data for vehicles on the road. In the case of futuristic transportation systems like ubiquitous transportation and smart highway, a method of data collection that is advanced enough to incorporate road lane recognition is required. To meet this requirement, technology based on radio frequency identification (RFID) has been researched. However, RFID may fail to yield accurate location information during high-speed driving because of the time required for communication between the tag and the reader. Moreover, installing tags across all roads necessarily incurs an enormous cost. One cost-saving alternative currently being researched is to utilize GNSS (global navigation satellite system) carrierbased location information where available. For lane recognition using GNSS, a precise digital map for determining vehicle position by lane is needed in addition to the carrier-based GNSS location data. A "precise digital map" is a map containing the location information of each road lane to enable lane recognition. At present, precise digital maps are being created for lane recognition experiments by measuring the lanes in the test area. However, such work is being carried out through comparison with vehicle driving information, without definitions being established for detailed performance specifications. Therefore, this study analyzes the performance requirements of a precise digital map capable of lane recognition based on the accuracy of GNSS location information and the accuracy of the precise digital map. To analyze the performance of the precise digital map, simulations are carried out. The results show that to have high performance of this system, we need under 0.5m accuracy of the precise digital map.

• 한국음향학회:학술대회논문집
• /
• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
• /
• pp.1052-1057
• /
• 1994

Dynamically localized self-organizing map model (DLSMM) is a new speech recognition model based on the well-known self-organizing map algorithm and dynamic programming technique. The DLSMM can efficiently normalize the temporal and spatial characteristics of speech signal at the same time. Especially, the proposed can use contextual information of speech. As experimental results on ten Korean digits recognition task, the DLSMM with contextual information has shown higher recognition rate than predictive neural network models.

• International Journal of Internet, Broadcasting and Communication
• /
• 제9권1호
• /
• pp.9-17
• /
• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

• 로봇학회논문지
• /
• 제3권1호
• /
• pp.33-41
• /
• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• 융합신호처리학회논문지
• /
• 제4권3호
• /
• pp.15-20
• /
• 2003

핸즈프리 마이크를 이용한 원격회의나 원격 통신 시스템과 같은 몇 가지의 응용분야에서, 음성 신호는 주위 잡음뿐만 아니라 마이크와 스피커사이의 결합에 의해 발생하는 에코에 의해서 왜곡되기 쉽다. 게다가 채널 왜곡이나 부가적인 잡음을 포함한 환경 잡음들은 원래의 입력 음성신호에 영향을 미치리라 고려된다. 본 논문에서는, 이러한 핸즈프리 음성에 있어서의 음성 인식률을 향상시키기 위해 에코 제거기와 최대 사후 추정(MAP)을 이용한 새로운 접근방식을 소개한다. 이 접근방식에서, 제안된 시스템이 에코를 포함한 주위 잡음 환경에서의 핸즈프리 음성인식에 효과적이라는 것을 보여준다 또한, 실험 결과는 에코 제거기와 MAP 환경적응 기술의 결합 시스템이 에코와 잡음 환경에 잘 적응하는 것을 보여준다.

• Spatial Information Research
• /
• 제2권2호
• /
• pp.197-205
• /
• 1994

본 논문에서는 여러 응용 분야의 GIS에서 기본 map으로 사용되는 지적도에 대하여 raster 영상데이타로부터 벡터(vector)영상을 구하는 벡터화 알고리즘 및 자동 지역 생성 알고리즘을 제시하고, 이들로부터 직선(edge)들로 이루어진 segment, region(polygon)등의 주요 지형 객체 인식 및 처리를 자동적으로 수행하는 고속 지형 객체 처리 소프트웨어 ARM(Automatic geographical en-tity Recognition and Modeling)에 대해서 설명한다.

• ETRI Journal
• /
• 제37권6호
• /
• pp.1220-1230
• /
• 2015

An autonomous valet parking (AVP) system is designed to locate a vacant parking space and park the vehicle in which it resides on behalf of the driver, once the driver has left the vehicle. In addition, the AVP is able to direct the vehicle to a location desired by the driver when requested. In this paper, for an AVP system, we introduce technology to recognize a parking space using image sensors. The proposed technology is mainly divided into three parts. First, spatial analysis is carried out using a height map that is based on dense motion stereo. Second, modelling of road markings is conducted using a probability map with a new salient-line feature extractor. Finally, parking space recognition is based on a Bayesian classifier. The experimental results show an execution time of up to 10 ms and a recognition rate of over 99%. Also, the performance and properties of the proposed technology were evaluated with a variety of data. Our algorithms, which are part of the proposed technology, are expected to apply to various research areas regarding autonomous vehicles, such as map generation, road marking recognition, localization, and environment recognition.

• 지구물리
• /
• 제9권2호
• /
• pp.99-103
• /
• 2006

3D object recognition independent of translation and rotation using an ultrasonic sensor array, invariant moment vectors and SOFM(Self Organizing Feature Map) neural networks is presented. Using invariant moment vectors of the acquired 16×8 pixel data of square, rectangular, cylindric and regular triangular blocks, 3D objects could be classified by SOFM neural networks. Invariant moment vectors are constant independent of translation and rotation. The recognition rates for the training and testing data were 95.91% and 92.13%, respectively.