JP2018017826A - Autonomous moving body and environment map update device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To disclose a technique that enables an environment map to be autonomously updated.SOLUTION: An autonomous moving body comprises environment map storage means, an observation device, map dependence type estimation position calculation means, credibility level calculation means and environment map update means. A credibility level of an estimation position calculated by the map dependence type estimation position calculation means falls from a prescribed value, an environment map is updated based on a distance to an obstacle and azimuth thereto observed by the observation device.SELECTED DRAWING: Figure 2

Description

  The technology disclosed in this specification relates to an autonomous mobile body. Specifically, the present invention relates to a technique for updating an environmental map stored in an autonomous mobile body.

  It stores an environmental map that shows the position of obstacles that exist in the area where the moving object moves, and has an observation device that observes the distance and direction from the moving object to the obstacle. A mobile body (hereinafter referred to as an autonomous mobile body) has been developed that calculates the position of the mobile body using the observation results, searches for a route from the position to the target position while avoiding obstacles, and moves.

  There are various types of obstacles, and there are obstacles that exist fixedly and obstacles that move. The moving obstacle is not reflected in the environmental map, and it may be necessary to correct or update the environmental map.

  Patent Document 1 discloses a technique for observing an environment while moving in an area and generating an environment map using the amount of movement of a moving object and observation data obtained by a distance sensor. In the technique of Patent Document 1, when a change in environment is recognized, a captured image of the part is presented to the operator. The operator determines whether or not it is necessary to update the environmental map of the relevant part, and updates to the latest environmental map based on this determination.

  In the technique of Patent Document 2, an operator sets an area where the environment is unchanged and an area where the environment is variable using an environment map. In the area where the environment is unchanged, the update is prohibited to suppress the accumulation of errors.

JP 2009-169845 A JP 2014-89740 A

  The technique of Patent Document 1 requires an operator's judgment as to whether or not to update the environmental map. That is, the environmental map cannot be updated autonomously by the autonomous mobile body itself. In the technique of Patent Document 2, an operator needs to set an area where the environment is unchanged and an area where the environment is variable in advance. Each time the moving area changes, operator processing is required. In any technique, it is not possible to determine whether or not to update the environmental map only with an autonomous mobile body. The present specification discloses a technique for determining whether or not the mobile body itself needs to update the environment map autonomously without requiring processing by an operator, and updating the environment map when the update is necessary. .

If the environment map is available, the distance and direction from the assumed position to the obstacle can be calculated from the environment map by assuming the position of the autonomous mobile body. Below, the information on the distance and direction to the obstacle calculated from the assumed position and environment map is referred to as a calculation result.
If an observation device for observing the distance and azimuth from the autonomous mobile body to the obstacle is prepared, information on the distance and azimuth from the autonomous mobile body to the obstacle can be specified from the observation result. Below, the information on the distance and direction to the obstacle observed with the observation device is referred to as an observation result.
It is possible to assume a plurality of positions and calculate the calculation result at each of the assumed positions. In this case, it can be assumed that the autonomous mobile body exists at an assumed position that yields a calculation result that best matches the observation result.

  However, the environment map is not always correct due to the presence of moving obstacles. In a situation where the environment map needs to be updated, the distance and direction information to the obstacle calculated from the environment map is incorrect and does not match the distance and direction information to the obstacle observed by the observation device. In a situation where the environmental map needs to be updated, there arises a problem that the exact position of the autonomous mobile body cannot be specified. In other words, in situations where the environment map needs to be updated, even if the distance and direction from the autonomous mobile object to the obstacle are observed by the observation device, the exact position of the autonomous mobile object cannot be identified. It cannot be updated. The technology for specifying the position using the environmental map and the technology for updating the environmental map have inherently incompatible factors. In this specification, the technique which makes both compatible is disclosed.

  Assuming multiple locations, using the environmental map, calculate the distance and direction to the obstacle at each assumed location (calculate the calculation result), and observe the distance and direction to the obstacle observed by the observation device (observation) According to the technique for estimating the position of the autonomous mobile body by comparing the result) and the calculation result, not only the position can be estimated but also the reliability of the estimated position can be calculated. For example, if the degree of coincidence between the calculation result and the observation result is high only at a specific assumption position, and the degree of coincidence between the calculation result and the observation result is low at all other assumption positions, it can be said that the reliability of the estimated position is high. . On the other hand, if the degree of coincidence between the calculation results and the observation results is almost uniform for all of the plurality of assumed positions, it can be estimated that the moving object is located in the vicinity of the plurality of assumed positions. Beyond that, a clear position cannot be estimated, and it can be said that the reliability of the estimated position is low.

  The present specification discloses a technique that employs an algorithm that mathematically calculates the credibility and updates the environment map when the credibility decreases.

  The autonomous mobile body disclosed in this specification includes an environment map storage unit, an observation device, a map-dependent estimated position calculation unit, a confidence calculation unit, and an environment map update unit. The environment map storage means stores an environment map indicating the position of an obstacle present in the area where the autonomous mobile body moves. The observation device observes the distance and direction from the autonomous mobile body to the obstacle. The map-dependent estimated position calculation means includes the assumed position where the autonomous mobile body is located on the environmental map, the distance and direction information to the obstacle calculated from the environmental map, and the obstacle observed by the observation device. The position of the autonomous mobile body is estimated from the distance and direction information. The reliability calculation means calculates the reliability of the estimated position. The environment map update means updates the environment map stored in the environment map storage means based on the distance and direction information to the obstacle observed by the observation device when the confidence level falls below a predetermined value.

  In the above autonomous mobile body, when the credibility falls below a predetermined value, the environment map is updated based on the distance and direction information to the obstacle observed by the observation device. That is, the environment map is updated when the probability that an error exceeding the allowable range is generated in the estimated position calculated by the map-dependent estimated position calculation means is higher than a predetermined value. For this reason, it is possible to determine whether or not the environmental map needs to be updated by the autonomous mobile body itself without requiring processing by the operator.

  Details of the technology disclosed in this specification and further improvements will be described in detail in the detailed description and examples.

The system configuration of a mobile object is shown. A system configuration when the mobile body performs an environmental map update process or the like is shown. 1 shows a system configuration when a moving body performs learning data generation processing. An example of the movement area | region which a mobile body moves at the time of learning is shown. An example of learning data is shown. The system configuration | structure when a mobile body implements reliability calculation formula production | generation processing is shown. The graph of a reliability calculation formula is shown. The graph of the weighting coefficient depending on reliability is shown. The flowchart of the environmental map update process which a moving body implements is shown. The figure which represented the environmental map update process content which a mobile body implements visually is shown.

  The main features of the embodiments described below are listed. The technical elements described below are independent technical elements and exhibit technical usefulness alone or in various combinations, and are limited to the combinations described in the claims at the time of filing. It is not a thing.

(Characteristic 1) An area in which the environment map is erroneous is moved to an autonomous mobile body, and an expression that is established between the probability distribution and the reliability is obtained.
(Feature 2) The observation device observes the distance to the obstacle in association with the azimuth while changing the azimuth with respect to the autonomous mobile body.
(Feature 3) The observation apparatus is mounted on an autonomous mobile body.
(Feature 4) The position of the point cloud located on the outline of the obstacle can be derived from the environment map.
(Feature 5) The position of the point cloud located on the contour of the obstacle can be derived from the observation device.
(Characteristic 6) The environment map update means is configured to change the point stored in the environment map to the point observed by the observation device when the distance observed by the observation device is shorter than the distance calculated from the environment map at the same azimuth. replace.

  An autonomous mobile body (hereinafter referred to as a mobile body) according to an embodiment will be described. The moving body is a wheel-driven moving body having wheels on the left and right, and autonomously travels within a predetermined area. The left and right wheels are driven independently, and the moving body can change the traveling direction by changing the rotational speed of the left and right wheels. As will be described later, the moving body repeatedly executes the process of estimating the position where the moving body exists. Each process is called a step. The “current step” to be described later means a step that is being performed by the mobile body. Also, “one step before” means “one step before the current step”, and “one step after (next) step” means “the next step after the current step (next)”. "Means. The moving body travels from the start point to the target position based on the estimated position calculated for each step. Note that the mobile body estimates its own position and the direction in which it is facing. Hereinafter, the position and orientation of the mobile body are collectively referred to as “position”.

  As shown in FIG. 1, the moving body 10 includes an encoder 12, a laser range finder 14 (hereinafter referred to as LRF 14), a triangulation sensor 16, and an arithmetic device 18.

  An encoder 12 is provided in each of the motor for driving the left wheel and the motor for driving the right wheel of the moving body 10, and each encoder 12 detects the left wheel rotation angle and the right wheel rotation angle independently. The left wheel rotation angle and the right wheel rotation angle detected by the pair of encoders 12 are input to the arithmetic unit 18.

The LRF 14 emits laser light and observes the time until the emitted laser light is reflected by an object and returned. From the time observed by the LRF 14, the distance from the LRF 14 (that is, the moving body 10) to the object is observed. Further, the azimuth angle (rotation angle from the axis fixed to the moving body) for emitting the laser light from the LRF 14 changes with time, and scans within a predetermined azimuth angle range. The LRF 14 can observe the distance to the obstacle in association with the azimuth angle with respect to the moving body 10. Hereinafter, the distance and direction from the moving body 10 to the obstacle are referred to as “observation results”. The observation result acquired by the LRF 14 is input to the arithmetic unit 18. The LRF 14 scans the laser light emission direction within a predetermined angle range with respect to the direction fixed to the moving body 10 (for example, within an angle range of 60 ° in the left-right direction).
The triangulation sensor 16 will be described later.

The arithmetic unit 18 includes a CPU 20 that performs arithmetic processing, a RAM 22 that temporarily stores arithmetic processing data, and a ROM 24 that stores arithmetic programs executed by the CPU. A part of the ROM 24 is an EEPROM, which can be rewritten.
The arithmetic unit 18 executes various processes described later. When the CPU 20 executes the calculation program stored in the ROM 24, the CPU 20 has a map dependent equation estimated position calculation unit 28, a map independent equation estimated position calculation unit 29 shown in FIG. 2, and a position error calculation unit 34 shown in FIG. And so on. Since the traveling control of the moving body 10 by the arithmetic device 18 can be performed by a known method, here, the estimated position calculation processing and the environment map update processing by the arithmetic device 18 will be described in detail.

(Map-dependent formula estimated position calculation process)
As shown in FIG. 2, the computing device 18 includes an environment map storage unit 26 and a map-dependent equation estimated position calculation unit (abbreviated as a first calculation unit) 28. The first calculation unit 28 calculates the estimated position of the moving object 10 repeatedly at time intervals.

  The environment map storage unit 26 is stored in the EEPROM of the ROM 24 and stores an environment map in which information indicating the position (x, y coordinates) and height (z coordinates) of the contour of an object existing in the moving area is recorded. I remember it. Objects stored in the environmental map include walls, furniture, and the like that obstruct movement. As will be described later, the environmental map is updated. When an obstacle that did not exist at the time of preparing the environment map moves and exists in the movement area, the presence of the obstacle is added to the environment map. Alternatively, when there are no obstacles that existed in the movement area at the time of preparing the environment map, an update is performed to delete the presence of the obstacles from the environment map.

The first calculation unit 28 repeatedly performs a Monte Carlo location identification method (Monte Carlo Localization (hereinafter referred to as MCL)) at time intervals, and calculates the estimated position of the moving object 10.
In the process of calculating the estimated position depending on the map by MCL,
(1) The integrated estimated position calculated in the previous step is used. When the current step is the first step after the moving body 10 starts moving from the start point, the “integrated estimated position calculated in the previous step” represents the position of the start point.
(2) The integrated estimated position calculated in the previous step is moved according to the motion model of the moving body 10, and the estimated position at the time of execution of the current step is calculated. At this stage, the estimated position is calculated according to the motion model.
(3) A set of particles distributed around the estimated position of (2) is generated. The term “particle” as used herein refers to an assumed position where calculation is performed assuming that the particle is located at the position, and includes the x-coordinate, y-coordinate, and yaw angle of the moving body 10.
(4) Next, for each particle, the distance to the obstacle with respect to the azimuth is calculated from the environment map acquired from the environment map storage unit 26 and the position and yaw angle of the particle. That is, the calculation result is obtained.
(5) The LRF 14 also observes the distance to the obstacle with respect to the azimuth. That is, the observation result is obtained.
(6) Therefore, the distribution of the distance to the obstacle (calculation result) with respect to the azimuth calculated in (4) above and the distribution of the distance to the obstacle (observation result) with respect to the azimuth observed in (5) above. Contrast.
(7) If the environment map is accurate, the calculation result of (4) matches the observation result of (5) for a specific single particle, and (4) and (5) for all other particles. Is inconsistent. In this case, it can be said that the probability that the moving body 10 is located on the specific particle is high, and the probability that the moving body 10 is located on another particle is low. That is, it is possible to calculate the probability that the moving body 10 is located in each particle from the degree of coincidence between the calculation result of (4) and the observation result of (5).
(8) If the probability is calculated for each particle, a probability distribution can be obtained. It can be assumed that the moving body 10 is located at a position where the probability distribution reaches a peak.
(9) There are cases where the selection of particles is inappropriate and the correct estimated position cannot be calculated. In this case, in the probability distribution, processing is performed to increase the particle density in a region where a high probability is distributed, and to decrease the particle density in a region where a low probability is distributed, and the processing after (4) is repeated. As a result, the distribution of the particles is optimized, and the correct estimated position can be calculated.

In the above, the center position of the particle is determined by (2). On the other hand, when the reference for determining the center position of the particles cannot be obtained, the calculation may be started assuming a group of particles that uniformly spread over the entire moving region. By the above-described particle group optimization processing, the particle distribution is optimized, and a correct estimated position can be calculated.
Instead of the above (2), an estimated position obtained by adding the latest one-step movement vector (obtained from the encoder 12) to the integrated estimated position calculated in the previous step may be used as the center position of the particle group. Good. Also in this case, the estimated position calculated in (8) is the position estimated depending on the map.

  The first calculation unit 28 performs the above processing until the actual position of the moving body 10 (hereinafter also referred to as a true value) is included in the set of particles, and the calculation result of any particle becomes very close to the observation result. repeat. In one step, the particle set distribution may be updated multiple times. Thereby, in the 1st calculation part 28, the estimated position of the moving body 10 in the present step is calculated. This estimated position is estimated from the environment map.

(Learning data generation process)
The mobile body 10 performs a travel called a learning travel before performing the autonomous travel. Below, the process which produces | generates learning data based on the learning driving | running | working of the moving body 10 and the data obtained at the time of learning driving | running | working is demonstrated. Note that the learning data is data used for processing for generating a reliability calculation formula described later.

  As shown in FIG. 3, the arithmetic unit 18 includes a true value calculation map storage unit 30, a true value calculation unit 32, a position error calculation unit 34, and a learning data generation unit in addition to each unit used for autonomous running. 36 and a learning data storage unit 38. The environment map storage unit 26 stores an environment map of an area in which the mobile body 10 performs learning travel. The region in which the mobile body 10 performs learning traveling may be different from the region in which autonomous traveling is performed. When the movement area is different between the learning traveling and the autonomous traveling, the environment map storage unit 26 stores the environment maps of both the movement areas. Below, in order to distinguish the movement area | region at the time of learning driving | running | working and the movement area | region at the time of autonomous driving | running | working, the former is called a "1st area | region" and the latter is called a "2nd area | region." In addition, the environment map of the first area is referred to as “first environment map”, and the environment map of the autonomous travel area is referred to as “second environment map”.

  FIG. 4 shows an example of the first region 40 in which the moving body 10 performs learning travel. As shown in FIG. 4, ridges 42 and 43 that are obstacles are arranged in a part of the first region 40. Reflectors 44 are fixed to the opposing surfaces (inner surfaces) of the flanges 42 and 43 at equal intervals. The travel of the moving body 10 during the learning travel is controlled by a remote controller (remote controller). A broken line indicates a travel route of the moving body 10 during the learning travel. As indicated by the broken line, the moving body 10 travels both in the region where the rods 42 and 43 are not disposed and in the region between the opposite rods 42 and 43.

  The first environment map stored in the environment map storage unit 26 is not complete. For example, the bag 42 is stored, but the bag 43 is not stored. Learning driving and learning data generation processing are performed under an incomplete environmental map.

  The true value calculation map storage unit 30 is provided in the ROM 24 and stores a map in which the positions of the eaves 42 and 43 and the reflector 44 are recorded. The environment map stored in the true value calculation map storage unit 30 is accurate and does not require updating.

  The triangulation sensor 16 emits laser light in almost all directions (360 °), and detects the laser light that is reflected by the reflector 44 and returns, so that the distance to the reflector 44 and the reflector 44 with respect to the moving body 10 are detected. The azimuth angle is observed and the information is output to the true value calculation unit 32 of the arithmetic unit 18. The triangulation sensor 16 is mounted on the moving body 10.

  The true value calculation unit 32 identifies the relative position of the moving body 10 with respect to the reflector 44 from the distance and azimuth information acquired from the triangulation sensor 16. Since the position of the reflector 44 is recorded in the map acquired from the map storage unit 30 for true value calculation, the position of the moving body 10 on the map is mapped by mapping the relative position of the moving body 10 on the map. Can be identified. In this way, the true value calculation unit 32 calculates the actual position (true value) of the moving body 10. This process is performed by the CPU 20. The true value of the moving body 10 is output to the position error calculation unit 34.

On the other hand, the first calculation unit 28 calculates the estimated position of the moving body 10 using the incomplete first environment map, and outputs the result to the position error calculation unit 34. The estimated position calculation process and the true value calculation process are performed at the same timing. Further, the first calculation unit 28 determines, for each particle, the standard deviation σ _{x in} the x direction, the standard deviation σ _{y in} the y direction, and the particle from the distribution of the latest particle set (that is, the particle set when the estimated position is calculated). The maximum value w _max of the calculated probability is calculated, and these values (standard deviation σ _x , standard deviation σ _y , maximum probability w _max ) are output to the learning data generation unit 36.

  The position error calculation unit 34 calculates a difference (position error) between the true value acquired from the true value calculation unit 32 and the estimated position acquired from the first calculation unit 28, and outputs the difference to the learning data generation unit 36. This process is performed by the CPU 20.

  When the position error acquired from the position error calculation unit 34 is 15 cm or less, the learning data generation unit 36 has the estimated position calculated by the first calculation unit 28 within the allowable range from the true value (that is, the position is successfully estimated). The estimation result is classified as “OK”. On the other hand, if the position error exceeds 15 cm, the estimated position calculated by the first calculation unit 28 is regarded as deviating from the true value by an allowable range or more (that is, position estimation has failed), and the estimation result is classified as “NG”. To do. In the learning run, the case where the position error is classified as “NG” is intentionally created. In other words, in the actual first area 40, the ridges 43 that are not recorded in the first environmental map are arranged. For this reason, when the moving body 10 travels between the eaves 42 and 43, the degree of matching between the observation result acquired by the LRF 14 and the calculation result calculated for each particle is low, and it is difficult to find a suitable particle. Become. This means that even if a true value is included in the particle set, the moving body 10 cannot find a particle that matches the true value as a matching particle. For this reason, when the moving body 10 travels between the eaves 42 and 43, a particle different from the true value is regarded as a matching particle, and the possibility of calculating the particle as an estimated position is increased. As a result, the position error between the estimated position and the true value becomes large, and when the position error exceeds 15 cm, it is classified as “NG”.

  As described above, the learning data generation unit 36 classifies the estimation results depending on the map on the basis of the position error, that is, on the basis of the shift amount of the estimated position of the moving body 10 with respect to the “true value”. In the present embodiment, the reference value of the allowable range in which the position estimation result is OK is set to 15 cm, but the reference value is not limited to this. As the reference value of the allowable range is lowered, the prediction accuracy by the reliability calculation formula can be increased.

FIG. 5 shows an example of learning data generated by the learning data generation unit 36. As shown in FIG. 5, the learning data generation unit 36, the calculation time of the estimated position depending on the map, the position error acquired from the position error calculation unit 34, the result of classifying the calculation result of the estimated position, and the first A data set group (learning data) in which data sets associated with the three variables (standard deviation σ _x , standard deviation σ _y , maximum probability w _max ) acquired from the calculation unit 28 are arranged in time series is generated (note that σ _(The alphabets shown in the columns of _x , σ _y and w _max are actually numerical values). By referring to the learning data, it is possible to know the position error at each time, the success / failure of the estimated position calculation result, and the variable when the estimated position is calculated. For example, since the position error between the estimated position and the true value is 7 cm at time t = 2s, the estimated position calculation result is classified as “OK”, and the variables σ _x , σ _y , and w _{max at} that time are , B, g, and l, respectively. At time t = 10 s, the position error between the estimated position and the true value is 20 cm. Therefore, the estimated position calculation result is classified as “NG”, and the variables σ _x , σ _y , and w _{max at} that time are , C, h and m, respectively. The learning data generation process is performed by the CPU 20.

  The learning data storage unit 38 is provided in the ROM 24 and stores the learning data generated by the learning data generation unit 36. The learning data stored in the learning data storage unit 38 is output to the reliability calculation formula generation unit 46 described later. In this way, by storing the learning data by the mobile body 10 itself, it is possible to verify whether there is an error by analyzing the log of the learning data when a malfunction occurs in the mobile body 10 or the like. Become. For this reason, it becomes easy to discover the cause when the malfunction occurs in the moving body 10, and the reliability of the moving body 10 can be improved. In this embodiment, the calculation time of the estimated position is included in the data set, but the present invention is not limited to this configuration. For example, the data set may not include time, and the learning data may be configured by a data group in which the data set is arranged regardless of time.

(Credibility calculation formula generation process)
A process in which the mobile object 10 generates a reliability calculation formula will be described. As shown in FIG. 6, the arithmetic unit 18 includes a credibility calculation formula generation unit 46 and a credibility calculation formula storage unit 48 in addition to the units used in the above-described processing.

（Ｚ：目的変数、Ｘ_１〜Ｘ_３：説明変数、ｂ_０：定数、ｂ_１〜ｂ_３：回帰係数）に学習データを適用して、定数ｂ_０及び回帰係数ｂ_１〜ｂ_３の値を求める。学習データを適用する際は、説明変数Ｘ_１、Ｘ_２、Ｘ_３に学習データの標準偏差σ_ｘ、標準偏差σ_ｙ、最大確率ｗ_ｍａｘのそれぞれ用いる。これにより、次の数式で表される信憑度計算式；

が生成される（図７参照）。ｐの値は、第１計算部２８により計算される推定位置がＮＧとなる確率である。ｐはまた、計算された推定位置の真値からのずれ量の大きさを示す尺度でもあり、ずれ量が大きいほど大きくなる。本明細書ではｐを不信度という。不信度ｐが所定値よりも大きくなったことは、計算した推定位置の信憑度が所定値より低下したことを意味する。
説明変数Ｘ_１〜Ｘ_３に用いる変数（σ_ｘ、σ_ｙ、ｗ_ｍａｘ）は互いに独立しているため、回帰係数ｂ_１〜ｂ_３は、項目間の影響が排除された係数となる。従って、不信度を算出する際に、より正確に項目ごとの影響度を量ることができる。信憑度計算式生成処理はＣＰＵ２０で行われる。
信憑度計算式記憶部４８は、ＲＯＭ２４に設けられており、信憑度計算式生成部４６で生成された信憑度計算式を記憶する。 The credibility calculation formula generation unit 46 acquires the learning data stored in the learning data storage unit 38 and performs logistic regression analysis on the learning data to generate a credibility calculation formula. Logistic regression analysis is a method of multivariate analysis, and when the result is binary, it is a method of generating a statistical regression model that can predict the probability that the result will occur. Specifically, a logistic model represented by the following formula:

(Z: objective _variable, X 1 to X _3: explanatory variable, _{b 0: constant,} b 1 ~b _3: regression coefficient) by applying the learning data, the value of the constant _{b 0} and regression coefficient _b 1 ~b ₃ Ask for. When applying the learning data, the standard deviation σ _x , the standard deviation σ _y , and the maximum probability w _max of the learning data are used for the explanatory variables X ₁ , X ₂ , and X ₃ , respectively. Thus, the reliability calculation formula represented by the following formula:

Is generated (see FIG. 7). The value of p is the probability that the estimated position calculated by the first calculation unit 28 is NG. p is also a scale indicating the amount of deviation of the calculated estimated position from the true value, and increases as the amount of deviation increases. In this specification, p is referred to as disbelief. The fact that the unreliability p is larger than a predetermined value means that the reliability of the calculated estimated position is lower than the predetermined value.
Since the variables (σ _x , σ _y , w _max ) used for the explanatory variables X _{1 to} X ₃ are independent from each other, the regression coefficients b _{1 to} b ₃ are coefficients in which the influence between items is eliminated. Therefore, the degree of influence for each item can be measured more accurately when calculating the disbelief. The reliability calculation formula generation process is performed by the CPU 20.
The credibility calculation formula storage unit 48 is provided in the ROM 24 and stores the credibility calculation formula generated by the credibility calculation formula generation unit 46.

(Credibility calculation processing)
The process in which the mobile body 10 calculates the unbelief p will be described. The arithmetic device 18 includes a reliability calculation unit 50 in addition to the units used in the above-described processing (see FIG. 2).

When the mobile body 10 performs autonomous traveling in the autonomous traveling region, the first calculation unit 28 calculates the estimated position using the autonomous traveling environment map (second environment map). The three variables σ _x , σ _y , and w _max when the current estimated position is calculated by the first calculation unit 28 are output to the reliability calculation unit 50.

The reliability calculation unit 50 uses the three variables σ _x , σ _y , and w _max acquired from the first calculation unit 28 as X ₁ , X ₂ , X of the reliability calculation formula acquired from the reliability calculation formula storage unit 48. ₃ is input to obtain the unbelief p in the current step. The unbelief p is calculated for each step. This process is performed by the CPU 20. The unbelief level p calculated by the confidence level calculation unit 50 is output to a map update determination unit 58 (described later). Note that, in the first step after the moving body 10 starts moving from the start point, the distrust level p that is a criterion for determination has not yet been calculated. The calculation of the unbelief p is performed from the second and subsequent steps.
The unbelief p is also transmitted to the weighting coefficient calculator 52. This will be described later.

(Map-independent calculation of estimated position)
As shown in FIG. 2, in addition to the first calculation unit 28 that calculates the map-dependent estimated position, the calculation device 18 calculates a map-independent estimated position calculation unit (second (Abbreviated as a calculation unit) 29, an integrated estimated position calculation unit 54, and an integrated estimated position storage unit 56. The integrated estimated position storage unit 56 is provided in the RAM 22 and stores the integrated estimated position (strictly, coordinates on the environment map) estimated by the integrated estimated position calculation unit 54 for each step. The second calculation unit 29 adds the movement amount vector for one step of the moving body 10 to the integrated estimated position in the previous step among the integrated estimated positions stored in the integrated estimated position storage unit 56. Is estimated as the position of the moving body 10 in the current step. The movement amount vector is available from the encoder 12. As is apparent from the above description, the second calculation unit 29 calculates the estimated position directly without using the environment map. The estimated position calculated by the second calculation unit 29 may include an element depending on the environment map. This is because a map-dependent element is included in the integrated estimated position in the previous step.

  In addition, the above-mentioned “integrated estimated position in the previous step” is, in other words, the newest integrated estimated position among the integrated estimated positions stored in the integrated estimated position storage unit 56. When the current step is the first step after the moving body 10 starts moving from the start point, the “integrated estimated position in the previous step” means the position of the start point.

(Integrated estimated position calculation process)
The mobile body 10 performs the above-described learning data generation processing and reliability calculation formula generation processing before performing autonomous traveling in the second region. And when the mobile body 10 carries out autonomous running, a weighting coefficient is calculated based on the reliability calculated from the reliability calculation formula, and the integrated estimated position is calculated using the weighting coefficient. Below, the integrated estimated position calculation process at the time of the autonomous traveling of the mobile body 10 is demonstrated. As shown in FIG. 2, the arithmetic unit 18 includes a weighting factor calculation formula storage unit 51, a weighting factor calculation unit 52, and an integrated estimated position calculation unit 54 in addition to the units used in the above-described processing.

（ｐ：不信度、α：重み係数）で表される（図８参照（後述））。
重み係数計算部５２は、重み係数計算式記憶部５１から取得した重み係数計算式に、信憑度計算部５０から取得した現在のステップにおける不信度ｐを入力して重み係数αを求める。この処理はＣＰＵ２０で行われる。重み係数計算部５２で算出された重み係数αは、統合推定位置計算部５４に出力される。 The weighting factor calculation formula storage unit 51 is provided in the ROM 24 and stores a preset weighting factor calculation formula. The weighting factor calculation formula is the following sigmoid function:

(P: disbelief, α: weighting coefficient) (see FIG. 8 (described later)).
The weighting factor calculation unit 52 obtains the weighting factor α by inputting the untrustworthiness p at the current step acquired from the reliability calculation unit 50 to the weighting factor calculation equation acquired from the weighting factor calculation equation storage unit 51. This process is performed by the CPU 20. The weighting factor α calculated by the weighting factor calculation unit 52 is output to the integrated estimated position calculation unit 54.

（ｒ_ｆ：統合推定位置、ｒ_１：地図に依存して推定した位置、ｒ_２：地図に依存しないで推定した位置）に従って計算を行い、移動体１０の現在のステップにおける統合推定位置を算出する。重み係数αは「地図に依存する推定位置」の重み係数として使用され、１から重み係数αを減じた係数１−αは「地図に依存しない推定位置」の重み係数として使用される。この処理は、ＣＰＵ２０で行われる。統合推定位置計算部５４で算出された統合推定位置は、統合推定位置記憶部５６と観測点群生成部６０（後述）に出力される。統合推定位置記憶部５６に出力されて記憶される統合推定位置は、１つ後のステップにおける地図に依存しない推定位置の計算処理に用いられる。 The integrated estimated position calculation unit 54 acquires the estimated position (estimated position depending on the map) acquired from the first calculation unit 28 and the second calculation unit 29 using the weighting coefficient α acquired from the weighting coefficient calculation unit 52. A weighted average value of the estimated position (estimated position not depending on the map) is calculated, and the value is set as the integrated estimated position. Specifically, the integrated estimated position calculation unit 54 calculates the following formula:

Calculation is performed according to (r _f : integrated estimated position, r ₁ : position estimated depending on the map, r ₂ : position estimated without depending on the map), and the integrated estimated position at the current step of the moving body 10 is calculated. To do. The weighting factor α is used as a weighting factor of “estimated position depending on the map”, and a coefficient 1−α obtained by subtracting the weighting factor α from 1 is used as a weighting factor of “estimated position not depending on the map”. This process is performed by the CPU 20. The integrated estimated position calculated by the integrated estimated position calculation unit 54 is output to the integrated estimated position storage unit 56 and an observation point group generation unit 60 (described later). The integrated estimated position that is output and stored in the integrated estimated position storage unit 56 is used for the calculation process of the estimated position that does not depend on the map in the next step.

  Here, the weighting coefficient calculation formula will be described with reference to the above function (Equation 3) and FIG. The weighting factor α calculated by the weighting factor calculation formula is a function of the unbelief p. The weighting factor α, which is a weighting factor of the estimated position depending on the map, decreases as the unbelief p increases (that is, as the estimation accuracy of the estimated position depending on the map decreases). Specifically, the value of the weighting factor α is close to 1 when the unbelief p satisfies 0 <p <0.1, 0.5 when p = 0.2, When 3 <p is satisfied, it is close to 0. For this reason, the integrated estimated position substantially coincides with the “estimated position depending on the map” when the unbelief p is less than 10%, and is “estimated position depending on the map” when the unbelief p is 20%. It is an intermediate point of the “estimated position independent of the map”, and almost coincides with the “estimated position independent of the map” when the disbelief exceeds 30%. Note that the value of the constant used by the weight coefficient calculation formula is not limited to the above values (50, 0.2). Further, the weight coefficient calculation formula is not limited to the sigmoid function. Various functions can be used depending on how the value of the weighting factor α is associated with the value of the unbelief p.

(Map update judgment process)
Next, the map update determination process will be described. When the moving body 10 travels in the vicinity of an obstacle that is not recorded in the autonomous travel environment map (second environment map) in the autonomous travel region, the calculation result calculated from the observation result acquired by the LRF 14 and the environment map Therefore, the accuracy of estimation of “estimated position depending on the map” is lowered. For this reason, the unbelief p becomes high. That is, even if a true value is included in the particle set, the moving object 10 cannot regard a particle corresponding to the true value as a conforming particle, and regards another particle as a conforming particle. In the moving body 10 of the embodiment, the first calculation unit 28 calculates the estimated position depending on the map in accordance with the MCL method. For this reason, when the moving body 10 travels in the vicinity of an obstacle that is not recorded in the second environment map, the estimated position depending on the map calculated by the first calculation unit 28 moves away from the true value. Eventually, the true value is not included in the particle set. Once the particle set no longer includes a true value, even after this, even if the mobile object 10 travels in a region where a correct environmental map is stored in the autonomous traveling region (second region) (that is, Even if useful observation results can be acquired), the MCL method may not be able to generate a particle set in a range including a true value. That is, it may be impossible for the first calculation unit 28 to accurately calculate the estimated position depending on the map. In order to avoid such a situation, the map update determination unit 58 increases the unbelief p. When the unbelief p exceeds 20%, the map update determination unit 58 outputs an environment map update command to the observation point cloud generation unit 60. . That is, the map update determination unit 58 does not output an environmental map update command when the unbelief p satisfies 0 <p ≦ 0.2 (when the reliability of the estimated position depending on the map is high), and 0 When .2 <p is satisfied (when the reliability of the estimated position depending on the map is low), an environmental map update command is output to the observation point group generation unit 60. This process is performed by the CPU 20. In this embodiment, a value at which the weight of the first calculation unit 28 and the weight of the second calculation unit 29 are 50:50 (that is, p = 0.2) is used as the threshold value for the environmental map update command. The unbelief p serving as a reference for determining whether or not to update the environmental map is not limited to the above. For example, it may be less than 20% or more than 20%.

(Environmental map update process)
Next, an example of processing in which the computing device 18 updates the environmental map will be described with reference to FIGS. First, the map point group generation unit 62 converts the environmental map (see A in FIG. 10) stored in the mobile body 10 into a map point group (see B in FIG. 10) represented by xy coordinate values. (S10). Next, the observation point group generation unit 60 converts the current observation result by the LRF 14 into an observation point group (see C in FIG. 10) represented by xy coordinate values (S12). The integrated estimated position at the current step is input from the integrated estimated position calculation unit 54 to the observation point group generation unit 60. The observation point group generation unit 60 generates an observation point group by converting the observation result by the LRF 14 to the environment map coordinate system (xy coordinate system) based on the integrated estimated position. The map point group and the observation point group converted in steps S12 and S14 are output to the point group matching unit 64, respectively. In addition, you may comprise the arithmetic unit 18 so that processing of step S12 and step S14 may be performed first.

  Next, the point group matching unit 64 combines the map point group and the observation point group (S16). An ICP (Iterative Closest Point) algorithm can be used for matching point clouds. ICP is a technique for performing matching by minimizing the sum of squares of the distances between two different point groups. Since ICP is a known technique, detailed description thereof is omitted. The point cloud (see D in FIG. 10) matched by the ICP is output to the environment map update unit 66.

  When the observation point group and the map point group are matched, an area representing the change in the environment appears. In practice, there may be obstacles that are recorded in the environmental map but not observed, and obstacles that are observed but not stored in the environmental map. The environment map update unit 66 generates the latest environment map by reflecting each change in the environment map stored in the moving body 10. Specifically, first, the environment map update unit 66 generates a free grid (see E in FIG. 10) in order to reflect the object removed from the current environment map stored in the mobile body 10 in the grid map. (S16). The free grid is a grid on the grid map indicating that there is no obstacle, and is between the current position (ie, the integrated estimated position of the current step) and the observation point group observed by the LRF 14. Is a free grid. That is, the observation point group matched with the map point group is formed into a grid by ICP, and a free grid is generated assuming that there is no obstacle (blank area) from the position (position) of the moving body 10 to the observation point group.

  Next, the environment map update unit 66 generates an occupied grid (see F in FIG. 10) in order to reflect the obstacle added to the current environment map stored in the moving body 10 in the grid map (S18). . The occupied grid is a grid on the grid map representing the presence of an object, and the point cloud of the change that does not exist in the map point group but exists in the observation point group is formed into an occupied grid. That is, by matching with ICP, a point cloud (difference point cloud) that is not in the map point cloud but exists in the observation point cloud is extracted, and the extracted grid is formed into a grid to generate an occuped grid.

  Next, the environment map update unit 66 generates the latest environment map (see G in FIG. 10) by superimposing the generated free grid and occupied grid on the environment map stored in the mobile object 10 (S20). . By executing the above processing, the latest environment map is generated. The environment map update unit 66 ends the process by updating the environment map currently stored in the mobile body 10 to the latest environment map (S22).

  The updated environment map (latest environment map) is used again when the first calculation unit 28 estimates the estimated position depending on the map. At this time, the observation result by the LRF 14 and the latest environment map are accurately matched, and the untrustworthiness of the first calculation unit 28 is reduced (the confidence is improved). For this reason, the map update determination unit 58 does not output a map update command, and the integrated estimated position calculation unit 54 places importance on the estimated position depending on the map (that is, in this embodiment, the distrust level p is less than 20%). The integrated estimated position is estimated. The mode of the environmental map update process is not limited to the above ICP. Various known methods can be used as long as the process of updating the environmental map is started when the credibility falls below a predetermined value.

  The effect of the moving body 10 of an Example is demonstrated. In this moving body 10, when the confidence level of the estimated position depending on the map is lower than a predetermined value (when the unbelief level p is higher than the predetermined value), based on the observation results of the distance to the obstacle and the azimuth angle. Generate the latest environmental map. That is, when the credibility of the estimated position of the first calculation unit 28 decreases, a process for updating the environment map is performed. Therefore, it is possible to determine whether or not the environmental map can be updated by the moving body 10 itself without requiring an operator's operation.

  In addition, since the point group converted into the coordinate system is used instead of the grid map when the observation result and the environment map are combined, an error due to the alignment can be reduced. In addition, the observation point group can be generated with high accuracy by estimating the integrated estimated position using the hybrid result of two types of estimated positions (the estimated position depending on the map and the estimated position not depending on the map). For this reason, the divergence between the observation point group and the map point group is reduced, and the accuracy of the point group matching by ICP can be improved.

  In addition, the weighting factor α used when the integrated estimated position calculation unit 54 estimates the integrated estimated position of the moving body 10 is a function of the unreliability p. The credibility calculation formula for calculating the unbelief p is an expression obtained by analyzing learning data acquired by the mobile object 10 learning and traveling using a logistic regression analysis method which is a method of machine learning. It is. Therefore, the weighting factor α is not affected by factors other than the reliability calculation formula. Therefore, for example, even if the moving area is changed or the administrator of the moving object 10 is changed, the reference for determining the value of the weighting coefficient α does not change. As a result, the estimation accuracy of the integrated estimated position of the mobile object 10 can be stabilized.

  In addition, the estimated position that does not depend on the map is obtained by adding the movement vector for one step calculated based on the rotation angles of the left and right wheels acquired from the encoder 12 to the integrated estimated position in the previous step. Desired. The information detected by the encoder 12 may include an error due to slipping of each wheel or the like. However, when the detection distance by the encoder 12 is relatively short, the above error can be almost ignored. Since the movement vector for one step is extremely short, the accuracy of the movement vector for one step is high. In addition, the estimated position not depending on the map is estimated without using the environmental map. For this reason, even when the moving body 10 autonomously travels in the vicinity of an obstacle that is not recorded in the environmental map, the estimation accuracy of the estimated position that does not depend on the map does not decrease due to this. . If the estimated accuracy of the integrated estimated position one step before is high, the estimated position not depending on the map in the current step can maintain high estimated accuracy.

  In the moving body 10 of the present embodiment, the weight coefficient α of the estimated position depending on the map decreases as the disbelief p increases, and the weight coefficient 1-α of the estimated position independent of the map increases. According to this configuration, when the disbelief p is high (that is, when the estimation accuracy of the estimated position depending on the map is low), the ratio of the estimated position depending on the map in the integrated estimated position is reduced and not dependent on the map. The proportion of estimated positions can be increased. As described above, the estimation accuracy of the estimated position independent of the map is high. For this reason, according to this structure, even if the estimation precision of the estimated position depending on a map falls, it can suppress that the estimation precision of an integrated estimated position falls. Further, since the weighting coefficient calculation formula is a continuous smooth function, the weighting coefficient α varies smoothly according to the transition of the unbelief p. Therefore, the integrated estimated position is a value that can continuously transition between the estimated position that depends on the map and the estimated position that does not depend on the map. Therefore, the estimation accuracy of the integrated estimated position is further improved.

  In the present embodiment, as described above, the integrated estimation position when the map update determination unit outputs the environmental map update command is advantageously the estimation result of the second calculation unit 29 that calculates the estimated position independent of the map. It has become. For this reason, the latest environment map can be generated with high accuracy.

In the present embodiment, information (that is, variables such as a position estimation result and a standard deviation σ _x ) that is the basis of the learning data is acquired by causing the moving body 10 to actually travel. For this reason, the calculation performance of the estimated position depending on the map of the moving body 10 is directly reflected in the learning data. Thus, by generating learning data based on information obtained by actually running, a more accurate reliability calculation formula can be generated.

  As mentioned above, although the Example of the technique which this specification discloses was described in detail, these are only illustrations, and the mobile body which this specification discloses contains what changed and changed the said Example variously. It is.

  For example, in the above-described embodiment, the position is estimated by the first calculation unit 28 and the second calculation unit 29, but the position may be estimated using an estimation method other than these. For example, the position can be estimated by using a camera or GPS.

  In the above-described embodiment, the average value of the estimated position that depends on the map and the estimated position that does not depend on the map is set as the integrated estimated position, and the process of updating the environment map based on the integrated estimated position is executed. Absent. For example, when the reliability of the first calculation unit 28 that calculates the estimated position depending on the map falls below a predetermined value, the estimated position depending on the map at the last time when the reliability is equal to or higher than the predetermined value (that is, Based on the estimated position at the present time and the observation result, the position obtained by adding the movement vector for one step to the estimated position depending on the map immediately before the reliability becomes less than the predetermined value) The latest environment map may be generated. Further, when the reliability of the estimated position by the first calculation unit 28 falls below a predetermined value, the estimated position depending on the map at the time when the reliability is equal to or higher than the predetermined value and the observation result observed at that time Based on this, the latest environmental map may be generated. Even if it is such a structure, there can exist an effect similar to the mobile body 10 of an Example. In the case of the latter method, it is not necessary to calculate an estimated position that does not depend on the map, and only an estimated position that depends on the map can be calculated. In this case, the estimated position depending on the map may be set as the integrated estimated position.

  Further, the first calculation unit 28 may calculate the estimated position depending on the map using an extended Kalman filter instead of the MCL. In this case, a confidence formula is generated using the values obtained from the estimated error covariance matrix (for example, the variance value, the value of each element of the error covariance matrix, the major axis and minor axis of the error ellipse) as explanatory variables. can do. Moreover, the estimated position estimation result depending on a map can be determined with high accuracy by inputting these variables obtained during autonomous driving into the reliability calculation formula.

  In the above embodiment, the movement information is acquired using the encoder 12 provided in the motor. However, the method of acquiring the movement information is not limited to this. For example, the movement information may be acquired using visual odometry using a camera or laser odometry using a laser sensor.

  In addition, the moving body 10 may not have the learning data generation unit 36, and the learning data may be generated outside the moving body 10. Further, the moving body 10 may not have the learning data storage unit 38. That is, the learning data may be deleted once the reliability calculation formula is generated based on the learning data. Further, the mobile object 10 may not have the credibility calculation formula generation unit 46, and the credibility calculation formula may be generated outside the mobile object 10.

Further, the reliability calculation formula generation unit 46 may use a support vector machine instead of the multivariate analysis. The support vector machine is a well-known technique in pattern recognition modeling, and is a technique for constructing an identification function that outputs a class to which a feature vector belongs in binary when a feature vector with an unknown class assignment is input. The discriminant function is composed of a training sample set with known class membership. For this reason, a discriminant function (credibility calculation formula) can be configured by using the learning data of the embodiment as a sample set for training and learning the learning data with a support vector machine. Then, by inputting the three variables σ _x , σ _y , and w _max acquired from the first calculation unit 28 into the identification function, it is possible to determine the estimated position estimation result depending on the map.

Further, the reliability calculation formula generation unit 46 may use a neural network instead of the multivariate analysis. The neural network is a well-known technique in pattern recognition modeling, and is a technique for constructing a discriminant function that outputs a class to which a variable belongs in binary when a variable with unknown class assignment is input. In this method, the discrimination function can be configured in two ways, supervised learning and unsupervised learning. In supervised learning, a learning example and a target output for the learning example are given, and the weight of the function in the initial state is adjusted so that the output obtained by inputting the learning example to the function in the initial state matches the target output Thus, an identification function is configured. For this reason, learning variables are learned by a neural network using the three variables σ _x , σ _y , and w _max of the learning data of the embodiment as learning examples and the classification result (OK / NG) at that time as a target output. The discriminant function (credibility calculation formula) can be constructed. Then, by inputting the three variables σ _x , σ _y , and w _max acquired from the first calculation unit 28 into the identification function, it is possible to determine the estimated position estimation result depending on the map.

  When analyzing learning data, discriminant analysis may be used instead of logistic regression analysis.

  In addition, the learning travel may be performed by simulation instead of actually traveling a specific area on the moving body.

  Further, the moving body 10 may not be a wheel drive type, and may be, for example, a small mobility that moves at a low speed, a leg type (such as bipedal walking), or a flying body. Further, the moving body 10 may have a plurality of LRFs 14.

  Further, in the true value calculation unit 32, the true value of the moving body 10 is calculated using the triangulation sensor 16 and the reflector 44, but the method of calculating the true value is not limited to this. For example, the true value may be calculated using a motion capture or magnetic rail technique.

  In this embodiment, a two-dimensional observation result is used for position estimation or the like, but a three-dimensional observation result obtained by a three-dimensional distance sensor or the like may be used.

  In addition, the environment map stored in the moving body 10 may be stored as a point group represented by coordinate values instead of the grid map. According to this configuration, the latest environmental map can be generated without the map point cloud generation unit 62 in FIG.

  In the technology disclosed in this specification, the map-dependent estimated position calculation means calculates the probability that an autonomous mobile object exists at the assumed position for each assumed position (particle), and estimates the position from the probability distribution. . A reliability calculation means calculates the reliability from the probability distribution. The reliability here is the probability that the estimated position stays within the allowable range from the true value.

  The autonomous mobile body disclosed in the present specification further includes map-independent position estimation means for estimating the position of the autonomous mobile body from the travel distance and the travel direction. In this case, when the credibility is reduced and the environment map is updated, the estimated position calculated last in the period when the credibility is equal to or higher than a predetermined value (this estimated position may be an estimated position depending on the map). The estimated position obtained by integrating the estimated position depending on the map and the estimated position not depending on the map may be obtained by adding the subsequent movement amount and the moving direction to the latest position and the observation device. The environmental map is updated based on the information on the distance and direction to the obstacles observed. Instead, when the credibility falls below a predetermined value, the information on the estimated position and the distance and direction to the obstacle observed by the observation device at that point in the period when the credibility was greater than or equal to the predetermined value. From this, the environmental map may be updated.

In the autonomous mobile body disclosed in this specification, the environment map is updated when the update is necessary, and thus the reliability of the estimated position depending on the map is high. Only the estimated position calculation unit depending on the map is sufficient, and the estimated position calculation unit independent of the map can be omitted.
The autonomous mobile body of the present embodiment has both a map-dependent estimated position calculation unit and a map-independent estimated position calculation unit, and integrates both to calculate an integrated estimated position. At that time, it is determined which of the estimated positions is to be emphasized based on the reliability of the estimated position calculation unit of the map-dependent formula. If the confidence level of the estimated position calculation part of the map-dependent formula is high, the position that emphasizes the estimated position depending on the map is regarded as the integrated estimated position, and if the confidence level of the estimated position calculation part of the map-dependent formula is low, the estimation independent of the map A position that places importance on the position is set as an integrated estimated position. The reliability of the integrated estimated position is high, and the reliability of the position of the autonomous mobile body used when updating the environmental map is high.

  The environmental map update device can be made independent, and the environmental map update device itself can be manufactured, sold, and used.

  The relationship between the component of an Example and the component of a claim is demonstrated. The environment map storage unit 26 according to the embodiment is an example of an environment map storage unit in the claims. The LRF 14 according to the embodiment is an example of the observation apparatus according to the claims. The 1st calculation part 28 of an Example is an example of the estimated position calculation means of the map dependence type | formula of a claim. The 2nd calculation part 29 of an Example is an example of the estimated position calculation means of the map independent formula of a claim. The credibility calculator 50 according to the embodiment is an example of a credibility calculator according to the claims. The observation point group generation unit 60, the map point group generation unit 62, the point group fitting unit 64, and the environment map update unit 66 according to the embodiment are examples of an environment map update unit.

  Specific examples of the present invention have been described in detail above, but these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and changes of the specific examples illustrated above. The technical elements described in this specification or the drawings exhibit technical usefulness alone or in various combinations, and are not limited to the combinations described in the claims at the time of filing. In addition, the technology illustrated in the present specification or the drawings achieves a plurality of objects at the same time, and has technical utility by achieving one of the objects.

10: moving body 12: encoder 14: laser range finder (LRF)
26: Environmental map storage unit 28: First calculation unit (map dependence type estimated position calculation unit)
29: Second calculation unit (map-independent formula estimated position calculation unit)
46: credibility calculation formula generation unit 48: credibility calculation formula storage unit 50: credibility calculation unit 51: weight coefficient calculation formula storage unit 52: weight coefficient calculation unit 54: integrated estimated position calculation unit 56: integrated estimated position storage unit 58: Map update determination unit 60: Observation point group generation unit 62: Map point group generation unit 64: Point group adjustment unit 66: Environmental map update unit

Claims (9)

An autonomous mobile,
An environmental map storage means for storing an environmental map indicating the position of an obstacle present in the area where the autonomous mobile body moves;
An observation device for observing the distance and direction from the autonomous mobile body to the obstacle;
Assumed position where the autonomous mobile body is assumed to be located on the environment map, information on distance and direction to the obstacle calculated from the environment map, information on distance and direction to the obstacle observed by the observation device From the map dependent estimated position calculation means for estimating the position of the autonomous mobile body,
A confidence calculating means for calculating the confidence of the estimated position by the map dependent position estimating means;
An environmental map update that updates the environmental map stored in the environmental map storage unit based on information on the distance and direction to the obstacle observed by the observation device when the confidence level falls below a predetermined value. means,
Autonomous mobile body equipped with.   2. The map-dependent estimated position calculation means calculates the probability that the autonomous mobile object exists at the assumed position for each assumed position, and estimates the position from the probability distribution. Autonomous mobile body.   The autonomous mobile body according to claim 2, wherein the confidence calculation means calculates the confidence from the probability distribution.   4. The autonomous mobile body according to claim 3, wherein the confidence calculation means calculates a confidence that the estimated position stays within an allowable range from a true value from the probability distribution. The autonomous mobile body further comprises map-independent position estimation means for estimating the position of the autonomous mobile body from a travel distance and a travel direction,
When the confidence level falls below the predetermined value, a latest position obtained by adding a subsequent movement amount and a moving direction to the last estimated position where the confidence level is equal to or greater than the predetermined value is obtained, and the latest position and the observation The autonomous mobile body according to any one of claims 1 to 4, wherein the environment map is updated from information on a distance to an obstacle and a direction observed by an apparatus.   When the reliability is lower than the predetermined value, from the last estimated position where the reliability was equal to or higher than the predetermined value, and information on the distance and direction to the obstacle observed by the observation device at that time, The autonomous mobile body according to any one of claims 1 to 4, wherein the environmental map is updated. The autonomous mobile body is
A map-independent estimated position calculating means for estimating the position of the autonomous moving body from a moving distance and a moving direction;
An apparatus for calculating an integrated estimated position by weighted averaging the estimated position by the map-dependent estimated position calculating means and the estimated position by the map-independent estimated position calculating means;
The autonomous mobile body according to claim 1, wherein a weight used for the weighted average is determined based on the reliability.   8. The method according to claim 1, wherein the map-dependent estimated position calculation means estimates the position using any one of a Monte Carlo location identification method (Monte Carlo Localization, MCL) and an extended Kalman filter. The autonomous mobile body described. An environmental map update device for an autonomous mobile body that has an observation device for observing the distance and direction to an obstacle and stores an environmental map that indicates the position of the obstacle present in the moving area;
Assumed position where the autonomous mobile body is assumed to be located on the environment map, information on distance and direction to the obstacle calculated from the environment map, information on distance and direction to the obstacle observed by the observation device From the map dependent estimated position calculation means for estimating the position of the autonomous mobile body,
Confidence calculation means for calculating the confidence of the estimated position by the estimated position calculation means of the map-dependent equation;
An environmental map that updates the environmental map stored in the environmental map storage means based on the distance and direction information to the obstacle observed by the observation device when the confidence level falls below a predetermined value. Update means,
An environmental map update device.

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