JP4032125B1 - Mobile object position estimation system and mobile object position estimation method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a moving body position estimation system and a moving body position estimation method capable of estimating the position of a moving body with high accuracy.
When position information of a moving object is acquired from a moving object position measuring device, a particle filter that samples particles dispersed in a coordinate system of map data using the position information is provided, and the particles sampled by the particle filter are used. A mobile object position estimation system and a mobile object position estimation method for estimating the position of the mobile object, the probability data indicating the probability that the mobile object exists in each area on the coordinate system of the map data, and the probability The position of the moving body is estimated with high accuracy by dispersing particles in the coordinate system of the map data according to the data.
[Selection] Figure 3

Description

  The present invention relates to a mobile body position estimation system and a mobile body position estimation method for estimating the position of a mobile body using position information obtained from a mobile body position measurement apparatus.

  In recent years, it has become possible to easily acquire position information of a moving body by a moving body position measuring device such as a GPS. However, the position information has low accuracy, and an error of several tens of meters to several hundreds of meters is generated in the mobile body position measuring device alone.

In order to reduce the uncertainty, a moving body position estimation system is used in which the position of the moving body is estimated by a computer based on position information obtained from the moving body position measuring device. The following Non-Patent Document 1 proposes a technique for probabilistically handling a position using a particle filter in a moving body position estimation system. The particle filter is a method of approximating the entire probability distribution by a weighted sum of a plurality of particles, and the position is estimated as follows. (1) N particles are generated at random positions on the coordinate system of the map data. (2) Randomly walk particles. (3) Position information from the moving body position measuring device is shown as a probability distribution. (4) The occurrence probability of each particle position is obtained according to the probability distribution. (5) The weight of each particle is updated in proportion to the occurrence probability. (6) Maximum likelihood estimation of the normal distribution for the position of the moving object from the weight and position of each particle. (7) The average of the normal distribution is estimated as the position of the moving object.
J. et al. Highwater, B.W. Borriello. “The Location Stack: A Layer Model for Location in Ubiquitous Computing”, 4th IEEE Workshop on Modular Computing Systems & Applications 2 (WMC). 22-28.2002.

  However, since the above prior art estimates the position of the moving body only by the measurement value from the moving body position measuring device, the estimation accuracy depends on the moving body position measuring device. The current position measurement by the mobile object position measurement apparatus includes an error of several tens of meters to several hundreds of meters. Therefore, the error also affects the position estimation, and it is difficult to perform accurate estimation. It was. In addition, since one normal distribution is obtained from the particles and the average is estimated as the position of the moving object, one estimated position is output. When the error of the moving body position measuring device is large, the aggregation of the estimation probabilities becomes large and the estimation accuracy becomes low when the estimation is made into one estimation value.

  Then, an object of this invention is to provide the mobile body position estimation system which can estimate the position of a mobile body with high precision, and a mobile body position estimation method.

  The mobile object position estimation system of the present invention includes a particle filter that samples particles dispersed in a coordinate system of map data using the position information when the position information of the mobile object is acquired from the mobile object position measurement device. In the moving body position estimation system that estimates the position of the moving body from the particles sampled by the filter, the moving body position estimation system has probability data indicating the probability that the moving body exists in each region on the coordinate system of the map data, and the probability data And a means for dispersing particles in the coordinate system of the map data. Further, according to the moving object position estimation method of the present invention, when the computer acquires the position information of the moving object from the moving object position measuring device, the particle filter uses the position information to sample particles dispersed in the coordinate system of the map data. In the moving object position estimation method for estimating the position of the moving object from the sampled particles, the probability data indicating the probability that the moving object exists in each region on the coordinate system of the map data is referred to. Dispersing particles in the coordinate system of the map data according to the data.

  Here, the area may be a physically distinguished area such as a building, road, or river, an artificially defined area such as an address, or an area arbitrarily set by the user. The probability data that the moving body exists in each area on the map may be arbitrarily set by the user, or may be set in the system in advance.

  The probability that a moving object exists differs depending on the area, for example, in the case of an information terminal carried by a person, roads and buildings are high and rivers and mountains are low. Moreover, it may differ depending on the movement pattern and movement range of the moving body. According to the present invention, the probability data indicating the probability that a moving body exists in each area on the map is referred to, and the particles are moved according to the probability. Therefore, the position reflecting the probability can be estimated. .

  In the probability data, it is preferable that the respective areas are classified into categories, and the probability can be set for each category.

  The probability that a moving body exists in each area is, for example, that if the moving body is a person, buildings and roads are high and the river is low, and if the moving body is a ship, the river is high and the land is low. Can be distinguished. According to the present invention, since the probability data can categorize each region and set the probability for each category, the probability of each region can be easily set according to the moving object. Can do.

  The map data preferably includes identification information for identifying each area, and the probability data is preferably classified into the areas using the identification information.

  In many cases, the map is color-coded for each area such as a road, a building, or a river, or is given a mark, and identification information for identifying each area is attached. According to the present invention, the probability data can be easily categorized using existing map data because the categories are divided using the identification information.

  The moving body position estimation system / method preferably includes means / steps for clustering the sampled particles and outputting an average value of each cluster as an estimated position of the moving body.

  When the position of the moving object is estimated from the whole particle, when the error in the position information from the moving object position measuring device is large, the variance of the estimation probability increases and the estimation accuracy decreases. According to the present invention, particles are clustered, and the average value of each cluster is used as the estimated position. Therefore, dispersion of the estimation probability within each cluster can be suppressed, and the estimation accuracy can be increased.

  According to the moving object position estimation stem / method according to the present invention, the moving object position estimation stem / method has probability data indicating the probability that a moving object exists for each region such as a road, a building, and a river. In order to reflect this, the position of the moving body can be estimated with higher accuracy.

  If the probability data area is divided into categories such as roads and rivers, and the probability can be set for each category, the probability suitable for the mobile object to be estimated can be easily set. The process of generating the probability data can be easily categorized using the existing map information by performing the categorization using the identification information attached to each area of the map.

  Also, by clustering particles and outputting the average value of each cluster as the position of the moving body, dispersion of estimation probabilities within each cluster can be suppressed, and estimation accuracy can be increased. In addition, a plurality of candidates can be obtained as estimated positions. For example, when searching for a mobile object, if the estimated position is output only as in the prior art, the search location is limited to one. According to the present system / method, a plurality of places can be searched as candidates, so that the possibility of discovery can be increased.

  The present invention can be applied to various fields. For example, a mobile object such as an information portable terminal with GPS function can be carried by a person and used to search for elderly people and victims who are hesitant or to ascertain the location of children who are going to and from school. be able to. In addition, if the moving body is mounted on an automobile or the like, it can be applied to the position management of a taxi or a delivery car.

  The mobile body position estimation system 1 according to the present embodiment includes a particle filter that samples the particles dispersed in the coordinate system of the map data using the position information when the positional information obtained from the mobile body position measurement device is acquired. This is a system for estimating the position of the moving object from the particles sampled by the particle filter.

  FIG. 1 is a block diagram showing a configuration of a moving object position estimation system 1 according to the first embodiment of the present invention. The mobile body position estimation system 1 according to the present embodiment connects an internal bus 11 with a communication interface 12, a CPU 13, a ROM 14, a RAM 15, a display 16, a keyboard / mouse 17, a drive 18, and a hard disk 19, and an address signal and a control signal. The computer system is configured to transmit data and the like and realize the moving object position estimation system according to the present embodiment.

  The communication interface 12 has various communication functions for communication with a mobile body position measurement device and a communication network such as the Internet, and receives position information of the mobile body from the mobile body position measurement device. It is also possible to download a program 19a or map data 19c that causes a computer to execute such position estimation. The CPU 13 controls the entire apparatus by the OS stored in the ROM 14 and controls the function of executing processing based on various application programs 19 a stored in the hard disk 19.

  The ROM 14 stores a program for controlling the entire apparatus, such as an OS, and has a function of supplying these to the CPU 13. The RAM 15 has a memory function used as a work area when the CPU 13 executes various programs.

  The display 16 has a function of graphically displaying map data, probability data, and an estimated position. The keyboard / mouse 17 can input data such as characters, numbers, symbols, etc., and can operate the point position on the screen.

  The drive 18 is a drive unit for executing installation work from a recording medium such as a CD or DVD in which various programs and data are recorded. It is also possible to install a program or map data that causes a computer to function as the moving body position estimation system 1 from a storage medium.

  The hard disk 19 is an external storage device that stores various data such as a program 19a, a memory 19b, and map data 19c used for position estimation. The program 19a corresponds to a program stored from the communication interface 12, the drive 18 and the like described above in the execution format. The memory 19b is a storage unit that stores files such as execution results of various programs.

  The map data 19c includes basic data D1 and probability data D2, for example, as shown in FIG. The basic data D1 includes data such as areas such as buildings, roads, rivers, prefectures, municipalities, and addresses, positions, longitude and latitude, directions, and distances. The probability data D2 includes information indicating the probability that a moving object exists in each area of the basic data D1. In the probability data D2, the areas are classified into categories such that the building is category a, the road is category b, the river is category c, and the others are category d, and the probability is set for each category. This probability is a probability that a mobile object to be estimated exists in the region. In the case of an information terminal carried by a person as an estimation target, the probability of category a corresponding to a building or category b corresponding to a road is high, and the probability of category c corresponding to a river is set low. However, the probability data D2 may be set for each area, such as each building or each road, without being classified into categories, or set in an artificially divided area such as an address. May be. Thereby, the action pattern and action range peculiar to a moving body can be reflected in the probability. The probability data D2 may be integrated as part of the map data 19c, or may be stored as separate data.

  The probability data D <b> 2 is generated by pre-processing before processing in step S <b> 1 described later, and is stored in the hard disk 19. The probability data D2 may be set in the system 1 in advance, or may be arbitrarily set or changed by the user. For example, the basic data D1 of the map data 19c is displayed graphically, the probability can be set directly for each building, road, etc., and the probability data D2 can be generated by setting the probability and category as a preset by the user. Also good. In addition, when the basic data D1 includes identification information for identifying an area such as a building or a road (for example, when a building or a road is color-coded or marked), the mobile object position estimation system 1 is The areas may be classified into categories using the identification information, and the categories may be used as the categories of the probability data D2. This eliminates the need for artificial categorization and makes it possible to easily categorize areas using existing map data.

  The particle in the present invention is an index indicating the position of the map data 19c on the coordinate system, and the attribute includes at least position information capable of specifying the position in the coordinate system of the map data 19c. The particle filter in the present invention has at least a function of sampling particles according to the position information from a plurality of particles dispersed in the coordinate system of the map data 19c when the position information obtained from the moving body position measuring device is acquired. In the present embodiment, the filter has a function of executing at least the following steps S1 to S5.

  FIG. 3 is a flowchart for explaining a mobile object position estimation method using the mobile object position estimation system 1. FIG. 4 is an explanatory diagram conceptually illustrating the position estimation process. The mobile body position estimation system 1 functions as follows to realize the mobile body position estimation method of the present invention.

  (Step S1) A plurality of particles are arranged at random positions in the coordinate system of the map data 19c. The arrangement of the particles is performed by setting the position information of each particle at random. As shown in FIG. 4A, particles (white circles in the figure) are arranged at random positions.

  (Step S2) With reference to the probability data D2 of the map data 19c, all particles are moved (random walk) depending on the probability. For example, as shown in FIG. 5, the probability data D2 is obtained by converting the latitude / longitude coordinate system of the basic data D1 into pixel units, and in each area divided into categories a, b, c, and d in the basic data D1. The probability is set with. Then, the particle movable range e is set within a constant radius circle centered on the particle position, and when the particles are randomly walked, the probability data of all the pixels in the movable range e is acquired, The next position is determined in proportion to the probability, and the particle is moved to that position. The timing for moving the particles is set in the moving body position estimation system 1 in advance, and the particles are randomly walked according to the probability data D2 at that timing. FIG. 4B is an explanatory diagram conceptually showing a state when particles are randomly walked according to probability data. Many particles are placed in a region with a high probability.

  (Step S3) While the position information of the moving object is not acquired from the moving object position measuring device, the process returns to Step S2 and continues the random walk. If the position information of the moving object is acquired from the moving object position measuring device, the process proceeds to step S4.

  (Step S4) When the mobile body position estimation system 1 acquires the position information, the mobile body position estimation system 1 converts the position information into data represented by the probability distribution P1 (x). The conversion from the position information to the probability distribution P1 (x) is performed according to a rule such as a mathematical formula set in advance. Since the format of the position information from the moving body position measuring device differs depending on the characteristics of the device, it is converted into a probability distribution according to the device. For this reason, the conversion rule by numerical formula etc. is preset for every moving body position measuring apparatus in the moving body position estimation system 1, and the conversion from position information to probability distribution P1 (x) is performed according to the rule. For example, in the case of GPS, latitude / longitude information is output, so that the latitude / longitude information obtained is converted into a normal distribution with an average. In addition, the position of the PHS is specified depending on which base station of the base stations located in each place has received data from the terminal. For this reason, the probability that the mobile body exists is uniform within the range in which the base station that has received the radio wave from the mobile body can receive the radio wave, and the probability is 0 in the other ranges. Therefore, when the location information is acquired from the PHS, the probability that the mobile body exists within the range in which the received base station can receive radio waves is set to a constant value (greater than 0) greater than 0, and the other ranges have the probability. Convert to a uniform distribution of zero.

  (Step S5) According to the probability distribution P (1), particles are sampled from the population on the map. In the sampling method, first, one particle is extracted from the population on the map with a probability according to the probability distribution P1 (x), and after returning it to the population, the next particle is similarly extracted again. By repeating this, the same number of particles as the population are extracted according to the probability distribution P1 (x). FIG. 6 is an explanatory diagram for explaining the probability distribution P1 (x) data when using GPS position information, the population on the coordinate system of the map data 19c, and the extracted particles. Particles are extracted from the population according to the normal distribution data P1 (x) with the acquired position information as an average. As for the particle, a part closest to the vertex (average) of the normal distribution is extracted, and a part farther away is not extracted. FIG. 4 (3) is an explanatory diagram for conceptually explaining the dispersion of particles sampled by the normal distribution. As for the particle, a part closest to the acquired position information is extracted, and a part farther away is not extracted.

(Step S6) The sampled particles are clustered. FIG. 4 (4) is an explanatory diagram for conceptually explaining the clustering of particles. Sampled particles are clustered into multiple clusters. Here, clustering according to the present embodiment will be described. For the clustering of the present embodiment, a clustering method using a normal mixture distribution P (x) is used. The normal mixture distribution is a probability distribution represented by Equation 1. P _i (x) in Equation 1 represents a normal distribution. The clustering process is to obtain the number c of normal distributions in Equation 1, the weight α _i of each normal distribution, and the average value and variance of each normal distribution p _i (x). In the present embodiment, these values are collectively called parameters of the normal mixture distribution P (x). Clusters generated by clustering correspond to the individual normal distributions constituting the normal mixture distribution P (x). When the parameters of the normal mixture distribution P (x) are obtained, it is possible to obtain probabilistically which cluster each particle belongs to according to Equation 1. Furthermore, when an arbitrary position on the map is given as an input, the probability that a moving body is present at that position can be obtained.

  FIG. 7 shows an example of clustering using the normal mixture distribution P (x). The rectangles on the horizon represent individual particles. P (x) represents a normal mixture distribution. The normal mixture distribution P (x) obtained from each particle has a higher probability (a mountain is higher) as the particles are gathered. In this example, two clusters are generated.

That is, in step S6, the moving body position estimation system 1 operates as follows. Equation 1 is set in advance in the moving object position estimation system 1, and the moving object position estimation system 1 uses the information on the particles sampled in step S5, and the number of parameters c in Equation 1 and the weight of each normal distribution. The average value and variance of α _i and normal distribution p _i (x) are calculated. Specifically, the weight α _i of each normal distribution and the average value and variance of the normal distribution p _i (x) are calculated based on an EM algorithm using clusters to which individual particles belong as hidden variables. The EM algorithm is a method widely known as a maximum likelihood estimation method in the case where there are hidden variables, and it has been proved that the solution converges by performing repeated calculations. On the other hand, the number c of the normal mixture distribution P (x) is incremented one by one until the value of the log likelihood of each particle in the normal mixture distribution P (x) shown in Equation 2 is decreased with the initial value set to 1. Ask. FIG. 4 (5) is an explanatory view for conceptually explaining the average value of each cluster. The average value of each calculated normal distribution p _i (x) is the coordinates (x, y) of the estimated position of the moving object.

  Furthermore, the moving body position estimation system 1 may calculate the probability P (x) that there is a moving body at each estimated position (x, y). In this case, the calculated parameters are substituted into Equation 1, and the coordinates (x, y) of the calculated estimated positions are used as the input information (x) of Equation 1, and P (x) is calculated.

  Note that the clustering method is not limited to the above, and may be non-hierarchical clustering that classifies a fixed number of clusters, or by combining clusters in order from the closest distance to each other. Hierarchical clustering such as a C-Means method for dividing into a large number of groups may be used, and the clustering method is not limited.

  (Step S7) A plurality of estimated positions (x, y) calculated in step S6 are output. In step 6, when the probability P (x) that the moving body exists for each estimated position (x, y) is calculated, the probability P (x) is displayed at each estimated position (x, y) or estimated. Position (x, y) may be ranked and output in descending order of probability P (x). The output destination may be the display 16 or may be output from the communication interface 12 to an information terminal (for example, a mobile phone) that can be connected via a network. Also, all of the calculated plurality of estimated positions may be output, or the top several may be output in order of probability.

  According to the mobile body position estimation method by the mobile body position estimation system 1, since the probability data D2 in which a mobile body exists for each region such as a road, a building, and a river is reflected, the position of the mobile body can be estimated with higher accuracy. It becomes. Further, since the position is estimated by clustering the particles, dispersion of the estimation probability within each cluster can be suppressed, and the estimation accuracy can be increased. Moreover, compared with the prior art in which the estimated position is output only, the present invention can obtain a plurality of candidates as estimated positions. For example, when the user is searching for a moving body, it is possible to search for a plurality of candidate sites.

(Second Embodiment)
In addition, for example, when the mobile object position estimation system 1 is used for a mobile object search such as a search for an elderly person, it is necessary for the searcher to go around a plurality of estimated positions to be output. Therefore, the mobile object position estimation system 1 may output a round route of a plurality of estimated positions. The cyclic route is output as follows. FIG. 8 is a flowchart for explaining the processing of this embodiment. This cyclic route processing (step S6-1) is performed after step S6 of the processing of the first embodiment is completed, and is realized by calculating the cyclic route and outputting the cyclic route together with the estimated position. The A description of the same parts as those in the first embodiment will be omitted.

  The moving body position estimation apparatus 1 is input with the starting point of the tour before step S6-1 of the tour route calculation. The start point may be arbitrarily input by the traveler, or may be input as position information obtained from the mobile body position measurement device using a function such as GPS of an information terminal carried by the traveler. The mobile body position measurement system 1 is highly likely that a patrol person will reach the mobile body from the position information of the start point and the calculated plurality of estimated positions and the probability that the mobile body exists at the estimated positions, and A route that can be visited in a short time is calculated.

Ｒｏｕｔｅは評価値Ｖを求めようとする経路における推定位置ｒ（スタート地点を除く各地点）の集合であり、ｒはその推定位置の１つを示す。Ｔ（ｒ）は、推定位置ｒに到達するまでの１つ前の地点（スタート地点又は推定位置）からの時間を求める関数である。Ｐ（ｒ）は、推定位置ｒの１つ前の地点までの全ての推定位置における移動体が存在する確率の総和である。評価値Ｖの値が小さいほど、効率的な巡回経路であるといえる。 For example, the moving body position estimation system 1 calculates a traveling route as follows. A route that circulates all the estimated positions from the start point (that is, the vertices of each normal distribution p _i (x) constituting the normal mixture distribution P (x) representing the estimated position) is searched. If the number of normal distributions p _i (x) constituting the normal mixed distribution P (x) is C, the path is the factorial C! Only exist. The following formula 3 is used as a function for evaluating these paths.

Route is a set of estimated positions r (points excluding the start point) on the route for which the evaluation value V is to be obtained, and r indicates one of the estimated positions. T (r) is a function for obtaining the time from the previous point (start point or estimated position) until the estimated position r is reached. P (r) is the sum of the probabilities that there are moving objects at all estimated positions up to the point immediately before the estimated position r. It can be said that the smaller the evaluation value V is, the more efficient the traveling route is.

  FIG. 9 is a graph showing an example of Equation (3). In FIG. 9, the horizontal axis indicates time, and the vertical axis indicates the probability that the moving object does not exist (1-P (x) in Equation 3), that is, the probability that the searcher cannot find the search target. Here, the number of normal distributions constituting the normal mixed distribution (that is, the number of estimated positions) C = 3 will be described as an example. The following processing is performed for all routes. The time required to reach the first normal distribution vertex (estimated position) p1 from the start point s and the probability that a moving object cannot be found at the reached vertex (estimated position) p1 are calculated. Further, the time required to reach the next vertex (estimated position) p2 from the first vertex (estimated position) p1 and the probability that a moving object at the reached vertex p2 cannot be found are calculated. In this way, the time and the probability that the moving object cannot be found are calculated in the order of the waypoints that circulate from the start point. Then, with the horizontal axis representing time and the vertical axis representing the probability that a moving body cannot be found, the calculated time of each vertex (estimated position) and the probability that a moving body cannot be found are plotted, and the integrated value (the hatched portion in FIG. 9). The size of (area) is the evaluation value V. This makes it possible to evaluate in consideration of the probability of finding a moving object and the traveling time. When outputting the cyclic route, only the route with the low evaluation value V may be presented, or all the cyclic routes may be output in the order of the evaluation value.

  As an example, as shown in FIG. 10A, calculation of a cyclic route when three estimated positions a, b, and c are obtained as a result of mobile object position estimation will be described. The starting point is the coordinates (0, 0), the estimated position a is the coordinates (5, 0), b is the coordinates (4, 3), c is the position of the coordinates (0, 5), and the movement at each estimated position The probability that a body exists is 0.5 for a, 0.1 for b, and 0.4 for c. FIG. 10B shows the result of calculating the evaluation value V for all the traveling routes. The result that the route that goes around in the order of abc is the best evaluation value is obtained, and abc is outputted as an effective tour route.

  As a result, the searcher can obtain information on a route through which the plurality of output estimated positions can be efficiently circulated. For example, in searching for an elderly person, it is possible to find the elderly person more quickly and ensure safety by patroling efficiently.

  Note that the present invention is not limited to the above-described embodiment, and can be modified without departing from the present invention. For example, the technique (step S2) that reflects the probability that a moving object exists for each region using probability data is applied to a conventional technique that performs maximum likelihood estimation of parameters without using clustering for position estimation. May be. This also reflects the probability for each region, and position estimation is performed with higher accuracy than in the past. In this case, the moving body position estimation system 1 performs processing as follows. FIG. 11 is a flowchart thereof. The mobile object position estimation system performs step 1 to step 5 above, then performs parameter maximum likelihood estimation from the sampling result to calculate the probability distribution, calculates the estimated position from the obtained probability distribution (step S6), The estimated position is output (step S7). The parameter maximum likelihood estimation and the calculation of the estimated position from the probability distribution may be performed according to the type of the position measuring device. For example, in the case of GPS, parameter maximum likelihood estimation is performed to calculate a normal distribution, and an average value of the normal distribution is output as an estimated position. In the case of PHS, in step 4, the probability that a moving body exists within the range in which the received base station can receive radio waves is set to a constant value greater than 0 (greater than 0), and the probability of other ranges is 0. Is converted into uniform distribution data. Except for step 4, the process is the same for both PHS and GPS.

  In addition, the present invention can be applied to various fields. For example, a mobile object such as an information portable terminal with GPS function can be carried by a person to search for an elderly person or a distressed person who is hesitant, or used to ascertain the position of a child who is going to and from school to ensure safety. it can. In addition, if the moving body is mounted on an automobile or the like, it can be applied to the position management of a taxi or a delivery car.

It is a block diagram which shows one structure of the moving body position estimation system by the 1st Embodiment of this invention. It is explanatory drawing which illustrates map information and probability data notionally. It is a flowchart explaining the mobile body position estimation method using the mobile body position estimation system in the said embodiment. It is explanatory drawing which illustrates notionally the position estimation process of the said embodiment. It is explanatory drawing explaining the setting of the probability data with respect to map information in the said embodiment. It is explanatory drawing explaining the probability distribution data when using the positional information of GPS, the population of the particle on map information, and the extracted particle. It is explanatory drawing which shows the example which represents the probability distribution of the particle extracted by the particle filter by normal mixture distribution. It is a flowchart explaining the process of 2nd Embodiment. It is explanatory drawing explaining the function used for calculation of the cyclic route of the said embodiment. It is explanatory drawing which shows the example of calculation of the cyclic route of the said embodiment. It is a flowchart explaining the process of other embodiment.

Explanation of symbols

1 Mobile object position estimation system 19c Map data D1 Basic data D2 Probability data

Claims (8)

When the position information of the moving object is acquired from the moving object position measuring device, a particle filter that samples particles dispersed in the coordinate system of the map data using the position information is provided, and the moving object is detected from the particles sampled by the particle filter. In a mobile position estimation system for estimating a position,
The movement having probability data indicating the probability that the moving body exists in each area on the coordinate system of the map data, and having means for dispersing particles in the coordinate system of the map data according to the probability data Body position estimation system.   The mobile object position estimation system according to claim 1, wherein the probability data has the respective areas classified into categories, and the probability can be set for each category.   3. The moving object position estimation system according to claim 2, wherein the probability data is classified into categories using identification information for identifying each area included in the map data.   The moving body position according to any one of claims 1 to 3, further comprising means for clustering the sampled particles and outputting an average value of each cluster as an estimated position of the moving body. system. When the computer acquires the position information of the moving object from the moving object position measuring device, the particle filter samples the particles dispersed in the coordinate system of the map data using the position information, and the position of the moving object is determined from the sampled particles. In the moving object position estimation method for estimating
The movement comprising the step of referring to probability data indicating the probability that the moving object exists in each area on the coordinate system of the map data, and dispersing particles in the coordinate system of the map data according to the probability data Body position estimation method.   6. The method according to claim 5, wherein in the probability data, each region is classified into categories, and the probability can be set for each category.   The movement according to claim 6, wherein the map data includes identification information for identifying each area, and the probability data includes the areas classified into categories using the identification information. Body position estimation method.   The moving body position according to any one of claims 5 to 7, further comprising a step of clustering the sampled particles and outputting an average value of each cluster as an estimated position of the moving body. Method.

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