• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.207-215
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• 2009

Recently, the research of the analysis of the crime spatial is increased by using the computer information technology and GIS (Geometric Information System) in order to prevent the urban crime so as to increase the urbanization rate. In this paper, a probability map formed by the raster is organized by the quantification of crime risk per the cell using the region property of the urban spatial information in the static environment. Also, a map of the risk probability is constructed based on the relative risk by the region property, the relative risk by the facility, the relative risk by the woody plant and the river, and so on. And, this integrated risk probability map is calculated by averaging the individual cell risk applied to the climatic influence and the seasonal factor. And, a probability map of the overall risk is generated by the interpretation key of the crime occurrence relative risk index, and so, this information is applied to the probability map quantifying the occurrence crime pattern. And so, in this paper, a methodology of the modeling and the simulation that this crime risk probability map is modified according to the passage of time are proposed.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.29-35
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• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.1
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• pp.23-31
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• 2020

It is essential to estimate the vehicle localization for an autonomous safety driving. In particular, since LIDAR provides precise scan data, many studies carried out to estimate the vehicle localization using LIDAR and pre-generated map. The road marking always exists on the road because of provides driving information. Therefore, it is often used for map information. In this paper, we propose to generate the Gaussian mixture map based on road-marking information and localization method using this map. Generally, the probability distributions map stores the single Gaussian distribution for each grid. However, single resolution probability distributions map cannot express complex shapes when grid resolution is large. In addition, when grid resolution is small, map size is bigger and process time is longer. Therefore, it is difficult to apply the road marking. On the other hand, Gaussian mixture distribution can effectively express the road marking by several probability distributions. In this paper, we generate Gaussian mixture map and perform vehicle localization using Gaussian mixture map. Localization performance is analyzed through the experimental result.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.693-699
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• 2006

An accurate and compact map is essential to an autonomous mobile robot system. For navigation, it is efficient to use an occupancy grid map because the environment is represented by probability distribution. But it is difficult to apply it to the large environment since it needs a large amount of memory proportional to the environment size. As an alternative, a topological map can be used to represent it in terms of the discrete nodes with edges connecting them. It is usually constructed by the Voronoi-like graphs, but in this paper the topological map is incrementally built based on the local grid map using the thinning algorithm. This algorithm can extract only meaningful topological information by using the C-obstacle concept in real-time and is robust to the environment change, because its underlying local grid map is constructed based on the Bayesian update formula. In this paper, the position probability is defined to evaluate the quantitative reliability of the end nodes of this thinning-based topological map (TTM). The global TTM can be constructed by merging each local TTM by matching the reliable end nodes determined by the position probability. It is shown that the proposed TTM can represent the environment accurately in real-time and it is readily extended to the global TTM.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.539-546
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• 2005

This paper describes a method of building the probability grid map for an autonomous mobile robot using the ultrasonic DAF(data association filter). The DAF, which evaluates the association of each data with the rest and removes the data affected by the specular reflection effect, can improve the reliability of the data for the Probability grid map. This method is based on the evaluation of possibility that the acquired data are all from the same object. Namely, the data from specular reflection have very few possibilities of detecting the same object, so that they are excluded from the data cluster during the process of the DAF. Therefore, the uncertain data corrupted by the specular reflection and/or multi-path effect, are not used to update the probability map, and hence building a good quality of a grid map is possible even in a specular environment. In order to verify the effectiveness of the DAF, it was applied to the Bayesian model and the orientation probability model which are the typical ones of a grid map. We demonstrate the experimental results using a real mobile robot in the real world.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.107-116
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• 2012

In this paper we propose a method that can generate the risk probability map in the form of raster shape by using Markov Chain methodology applied to the object interpretation keys and quantified risk indexes. These object interpretation keys, which are primarily characteristics that can be identified by the naked eye, are set based on the objects that comprise the spatial information of a certain urban area. Each key is divided into a cell, and then is weighted by its own risk index. These keys in turn are used to generate the unified risk probability map using various levels of crime prediction probability maps. The risk probability map may vary over time and means of applying different sets of object interpretation keys. Therefore, this method can be used to prevent crimes by providing the ways of setting up the best possible police patrol beat as well as the optimal arrangement of surveillance equipments.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.135-144
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• 2009

In this paper, a methodology for the risk probability map generation of the crime prevention to be subject to the urban area in the group residential area is presented. The interpretation key is set up to the distinctive feature distinguishing with the unaided eye based on the object composing with the urban area information such as the topology, the facility, and the characteristic information of the corresponding area by analyzing the crime prevention case occurred by gone. This interpretation key is generated, and this information is applied to another area equally, and so, the risk probability map for the crime prevention and the disaster prevention is generated. At this time, the object interpretation key for the urban area information is divided into the various size cell by the crime prevention case. and the risk index according with this cell is set up. Also, the generated various risk probability map is unified, and the integration risk probability map is generated.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.63-68
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• 2017

In this paper, we propose a bounding box prediction algorithm using multiple probability maps to improve object detection result of object detector. Although the performance of object detectors has been significantly improved, it is still not perfect due to technical problems and lack of learning data. Therefore, we use the result correction method to obtain more accurate object detection results. In the proposed algorithm, the preprocessed bounding box created as a result of object detection by the object detector is clustered in various form, and a conditional probability is given to each cluster to make multiple probability map. Finally, multiple probability map create new bounding box of object using morphological elements. Experiment results show that the newly predicted bounding box reduces the error in ground truth more than 45% on average compared to the previous bounding box.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.304-310
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• 2009

This paper proposes a successive MAP (maximum a posteriori probability) detection scheme with SoIC(soft interference cancellation) to reduce the receiver complexity of hierarchical M-ary QAM system. For the successive MAP detection, modulation symbols generated from the other data streams are treated as Gaussian noise or eliminated as the soft interference according to their priorities. The log-likelihood ratio of the a posteriori probability (LAPRP) of each bit is calculated by the MAP detector with an adjusted noise variance in order to take the elimination and Gaussian assumption effect into account. By separating the detection process into the successive steps, the detection complexity is reduced to increase linearly with the number of bits per hierarchical M-ary QAM symbol. Simulation results show that the proposed detection provides a small performance degradation as compared to the optimal MAP detection.