JP2021018638A - Self-position estimation device - Google Patents

Abstract

To provide a self-position estimation device capable of achieving a self-position estimation with higher accuracy about a movable body.SOLUTION: A self-position estimation device 2 for estimating a self-position of a movable body 1 comprises: a sensor part 21 for acquiring information used for position estimation of the movable body 1; a first position estimation part 22 for performing position estimation of the movable body 1 using information acquired by a first sensor 31 of the sensor part 21, and acquiring first reliability about the estimation result; a second position estimation part 23 for performing position estimation of the movable body 1 using information acquired by a second sensor 32 of the sensor part 21, and acquiring second reliability about the estimation result; and an acquisition part 24 for acquiring a self-position of the movable body 1 according to the estimation result corresponding to the higher reliability out of the first and second reliability. Since the self-position is acquired using the estimation result with higher reliability, the self-position with higher accuracy can be acquired.SELECTED DRAWING: Figure 1

Description

The present invention relates to a self-position estimation device that estimates the self-position of a moving body.

Conventionally, the self-position of a moving body has been acquired and moved, and it has been studied to acquire the self-position with high accuracy. For example, in order to maintain the reliability of the positions of the entire plurality of moving bodies in a high state, the movement control is performed so as to restore the reliability of the self-position (see, for example, Patent Document 1). Further, for example, in order to stabilize the accuracy of self-position estimation using a navigation satellite, the self-position is estimated by selecting a combination of navigation satellites that can always be received (for example, Patent Document 2). reference). Further, for example, position estimation is performed by integrating the result of environmental measurement using a stereo camera or the like and the information acquired by using an encoder or the like (see, for example, Patent Document 3).

Japanese Unexamined Patent Publication No. 2017-188066 Japanese Unexamined Patent Publication No. 2017-182692 Japanese Unexamined Patent Publication No. 2008-234350

The technique described in Patent Document 1 improves the accuracy of position estimation by using a plurality of moving bodies, and is not effective in improving the accuracy of self-position estimation in a single moving body.

Further, the technique described in Patent Document 2 uses a plurality of satellites to estimate the position, and the selection criterion of the plurality of satellites is whether or not the target satellite can be used. Therefore, for example, it is possible to prevent the positioning accuracy from being lowered by avoiding the situation where the satellite signal used for the position estimation cannot be received. On the other hand, when moving in a space with many obstacles, it is possible that multiple satellites that are not affected by obstacles cannot be selected, and in such a situation, the accuracy of position estimation may decrease. There is.

Further, in the technique described in Patent Document 3, the position is estimated by integrating the result of the environmental measurement and the information acquired by using an encoder or the like. Therefore, for example, when the accuracy of one of them is lowered, However, there is a problem that the accuracy of the integration result is lowered.

The present invention has been made to solve the above problems, and an object of the present invention is to provide a self-position estimation device capable of realizing more accurate self-position estimation for a moving body.

In order to achieve the above object, the self-position estimation device according to the present invention is a self-position estimation device that estimates the self-position of a moving body, and has a sensor unit and a sensor that acquire information used for estimating the position of the moving body. Of the plurality of position estimation units that estimate the position of the moving object using the information acquired by the unit and acquire the reliability of the estimation result, and the plurality of reliabilitys acquired by each of the plurality of position estimation units. It is provided with an acquisition unit that acquires the self-position of the moving body according to the estimation result corresponding to the highest reliability.
With such a configuration, it is possible to acquire the self-position according to the estimation result with the highest reliability, and it is possible to realize more accurate self-position estimation. Further, even if a plurality of moving bodies do not exist, the accuracy of self-position estimation in a single moving body can be improved. Further, for example, a certain position estimation unit estimates the position by GPS (Global Positioning System), and it is reliable even when moving in an environment with many multipaths such as an environment with many obstacles. By using the result of position estimation other than GPS by another position estimation unit depending on the degree, it is possible to acquire the self-position with high accuracy. In addition, even if the accuracy of one of the estimation results is significantly reduced by acquiring the self-position according to the estimation result with the highest reliability among the plurality of estimation results, the other estimation results are obtained. By acquiring the self-position according to the above, it is possible to avoid a decrease in the accuracy of the self-position.

Further, in the self-position estimation device according to the present invention, the sensor unit has a plurality of sensors which are sensors for acquiring different information, and the plurality of position estimation units use a plurality of information acquired by the plurality of sensors. The position of the moving body may be estimated.
With such a configuration, a plurality of estimation results can be acquired by using the information acquired by each of the plurality of sensors. Therefore, when the reliability of the estimation result acquired by using the information acquired by one of the sensors is low, the self-position is determined according to the estimation result acquired by using the information acquired by the other sensor. To be acquired. Each sensor has its own strengths and weaknesses, but by acquiring such self-position, the weaknesses can be complemented with each other, and more accurate self-position acquisition can be realized.

Further, in the self-position estimation device according to the present invention, the plurality of position estimation units may estimate the position of the moving body by different methods using the same information acquired by the sensor unit.
With such a configuration, the self-position is acquired according to the estimation result acquired by a more reliable method. The accuracy may differ depending on the method of estimating the position, but by acquiring the self-position in this way, it is possible to acquire the self-position with higher accuracy. For example, when estimating the position using a probabilistic method, the estimation result may differ even if the parameters and the like are slightly changed. In such a case, the accuracy is improved by acquiring the self-position using the estimation result of the estimation method having the highest reliability.

Further, in the self-position estimation device according to the present invention, the plurality of position estimation units estimate the position of the moving body, and the acquisition unit determines the estimated position corresponding to the highest reliability among the plurality of reliabilitys. It may be selected as the self-position of the moving body.
With such a configuration, the estimated position with the highest reliability can be set as the self-position of the moving body, and the process of acquiring the self-position using the estimation result is only selection. Therefore, the process of acquiring the self-position Is a simple process.

Further, in the self-position estimation device according to the present invention, the plurality of position estimation units estimate the amount of movement to be added to the previous self-position, and the acquisition unit has a plurality of reliabilitys of the previous self-position. Of these, the self-position may be acquired by adding the movement amount corresponding to the highest reliability.
With such a configuration, even when the estimation result used for acquiring the self-position is changed from one estimation result to another depending on the reliability, the self-position after the change is greatly changed. Can be avoided. Therefore, the estimation results can be switched seamlessly, and smooth self-position estimation can be realized.

According to the self-position estimation device according to the present invention, it is possible to acquire the self-position according to the estimation result with the highest reliability, and it is possible to realize more accurate self-position estimation.

A block diagram showing a configuration of a moving body according to an embodiment of the present invention. A flowchart showing the operation of the self-position estimation device according to the same embodiment. A block diagram showing an example of another configuration of a moving body according to the same embodiment.

Hereinafter, the self-position estimation device according to the present invention will be described with reference to embodiments. In the following embodiments, the components and steps having the same reference numerals are the same or correspond to each other, and the description thereof may be omitted again. The self-position estimation device according to the present embodiment acquires the self-position according to the estimation result having the highest reliability among the estimation results regarding the position of the moving body estimated by the plurality of position estimation units.

FIG. 1 is a block diagram showing a configuration of a moving body 1 according to the present embodiment. The moving body 1 according to the present embodiment moves by acquiring a self-position and using the self-position, and the self-position estimation device 2 for estimating the self-position of the moving body 1 and the moving mechanism 11 move. It includes a control unit 12. Further, the self-position estimation device 2 includes a sensor unit 21, a first position estimation unit 22, a second position estimation unit 23, and an acquisition unit 24. In the present embodiment, the case where the self-position estimation device 2 includes the two position estimation units 22 and 23 will be mainly described, but it may not be the case. The self-position estimation device 2 may include three or more position estimation units, as will be described later.

The moving body 1 may be, for example, a traveling body traveling on land, a flying body flying in the air, or a surface navigation body (for example, a ship) moving on water. Often, it may be an underwater navigator (eg, a submersible) that moves underwater. In the present embodiment, the case where the moving body 1 is a traveling body will be mainly described.

The moving body 1 which is a traveling body may be, for example, a dolly, an autonomous driving vehicle, or a moving robot. The robot may be, for example, an entertainment robot, a monitoring robot, a transfer robot, a cleaning robot, or a robot that shoots a moving image or a still image. , Other robots may be used.

The air vehicle may be, for example, a rotary wing aircraft, an airplane, an airship, or another air vehicle. From the viewpoint of being able to move to an arbitrary position, the flying object is preferably a rotary wing aircraft. The rotorcraft may be, for example, a helicopter or a multicopter having three or more rotors. The multicopter may be, for example, a quadcopter having four rotor blades, or may have another number of rotor blades.

Further, the moving body 1 may move autonomously, for example. Note that the autonomous movement of the moving body 1 may mean that the moving body 1 does not move in response to an operation instruction received from a user or the like, but moves to a destination at its own discretion. The destination may be, for example, manually determined or automatically determined. Further, the movement to the destination may or may not be performed along the movement route, for example. Further, moving to the destination by one's own judgment may mean, for example, moving to the destination by the moving body 1's own judgment of the traveling direction, movement, stop, and the like. Further, for example, the moving body 1 may move so as not to collide with an obstacle.

Next, the configuration of the self-position estimation device 2 of the moving body 1 will be described.
The sensor unit 21 acquires information used for estimating the position of the moving body 1. The estimation regarding the position of the moving body 1 may be an estimation regarding only the position of the moving body 1 or an estimation regarding the position and posture (angle) of the moving body 1. In the present embodiment, the latter case will be mainly described. Here, as shown in FIG. 1, a case where the sensor unit 21 includes the first sensor 31 and the second sensor 32 will be mainly described, and other cases will be described later. The first and second sensors 31 and 32 are sensors that acquire different information. The sensor that acquires different information may be, for example, a sensor that acquires information by different types of sensing (for example, an image sensor and a distance measuring sensor), and acquires information on different objects by the same type of sensing. Sensors (for example, a distance measuring sensor that measures a range in one direction (front range, etc.) of the moving body 1 and a distance measuring sensor that measures a range in another direction (rear range, etc.)) There may be. The first and second sensors 31 and 32 are not particularly limited, but independently, for example, a sensor that acquires information around the moving body 1, an angular position sensor, an acceleration sensor, a GPS sensor, and the like. It may be. The sensor that acquires the information around the moving body 1 may be, for example, a distance measuring sensor, an image sensor, or the like. It is preferable that the acquisition of information by the first and second sensors 31 and 32 is performed synchronously. This is because it is preferable that the first and second position estimation units 22 and 23 estimate the positions of the simultaneous points.

Further, when the estimation of the position of the moving body 1 is the estimation of the position and the posture of the moving body 1, the first sensor 31 and the second sensor 32 may have two or more sensors. .. For example, the first sensor 31 and / or the second sensor 32 may have an acceleration sensor and a sensor used for detecting the posture of the moving body 1, a GPS sensor and the posture of the moving body 1. It may have a sensor used for detecting the above. The sensor used for detecting the posture of the moving body 1 may be, for example, a gyro sensor or a geomagnetic sensor (electronic compass). The gyro sensor may, for example, detect the posture of the moving body 1, or may detect the angular velocity or the angular acceleration of the moving body 1.

The distance measuring sensor is a sensor that measures the distance to a surrounding object in a plurality of directions. For example, a laser sensor, an ultrasonic sensor, a distance sensor using microwaves, or a stereo image taken by a stereo camera is used. It may be a distance sensor or the like. The laser sensor may be a laser range sensor (laser range scanner). The distance measuring sensors are already known, and the description thereof will be omitted. In the present embodiment, the case where the ranging sensor is a laser range sensor will be mainly described. Further, the distance measuring sensor may have one laser range sensor, or may have two or more laser range sensors. In the latter case, two or more laser range sensors may cover all directions. Further, when the distance measuring sensor is an ultrasonic sensor, a distance sensor using a microwave, or the like, the distance in a plurality of directions may be measured by rotating the distance measuring direction of the distance measuring sensor. Distances in a plurality of directions may be measured using a plurality of distance measuring sensors arranged in. Measuring the distance in a plurality of directions may mean, for example, measuring the distance in a plurality of directions at a predetermined angle interval for a predetermined angle range or the entire circumference (360 degrees). The angular interval may be constant, for example, 1 degree interval, 2 degree interval, 5 degree interval, and the like. The information acquired by the distance measuring sensor may be, for example, the distance to a peripheral object for each of a plurality of azimuth angles with respect to a certain direction of the moving body 1. By using the distance, it becomes possible to know what kind of object exists around the moving body 1 in the local coordinate system of the moving body 1.

The image sensor may be, for example, a CCD image sensor, a CMOS image sensor, or the like. Note that these image sensors are already known, and their description will be omitted. The image sensor may include, for example, an optical system such as a lens for forming an image on the image sensor. Further, the image sensor may be monocular or binocular (stereo camera). The image sensor usually captures a moving image, that is, acquires a continuous image frame. Since the captured image is acquired in order to acquire the self-position of the moving moving body 1, it is preferable that the frame rate is sufficiently large with respect to the moving speed. For example, the frame rate may be about 30 fps or the like.

The angular position sensor may be, for example, a rotary encoder or the like. The angular position sensor is already known, and the description thereof will be omitted. The angular position sensor may be used, for example, to detect the amount of rotation of a drive shaft such as a wheel of the moving body 1. The information acquired by the angular position sensor may be, for example, the amount of rotation or the angle of the rotating shaft.

The acceleration sensor measures the acceleration of the moving body 1. This acceleration sensor may be, for example, a two-axis acceleration sensor or a three-axis acceleration sensor. The acceleration sensor is already known, and the description thereof will be omitted. When the moving direction is limited to a predetermined plane (for example, when the moving body 1 is a traveling body moving on the floor surface), a biaxial acceleration sensor may be used. The information acquired by the acceleration sensor may be, for example, the acceleration of the moving body 1.

A GPS sensor is a sensor that receives radio waves from GPS satellites. The GPS sensor is already known, and the description thereof will be omitted. The information acquired by the GPS sensor may be, for example, a radio wave transmitted from a GPS satellite.

The information acquired by the gyro sensor may be, for example, the posture (angle) of the moving body 1, the angular velocity, and the angular acceleration. Further, the information acquired by the geomagnetic sensor may be, for example, the posture of the moving body 1. The posture of the moving body 1 may be, for example, the azimuth angle of the moving body 1, the azimuth angle and the elevation / depression angle of the moving body 1.

The first and second position estimation units 22 and 23 estimate the position of the moving body 1 by using the information acquired by the first and second sensors 31 and 32 of the sensor unit 21, respectively, and estimate the position thereof. Obtain the reliability of the estimation result. It should be noted that the first and second position estimation units 22 and 23 shall perform estimation of positions and acquisition of reliability according to the types of the first and second sensors 31 and 32, respectively. For example, when the first sensor 31 is a distance measuring sensor, the first position estimation unit 22 estimates the position using the information acquired by the distance measuring sensor and acquires the reliability, and the first position estimation unit 22 obtains the reliability. When the sensor 31 is an image sensor, the first position estimation unit 22 estimates the position and acquires the reliability using the information acquired by the image sensor. Therefore, the first and second position estimation units 22 and 23 are not particularly limited, but are independent of each other, for example, the information and the angle acquired by the sensor that acquires the information around the moving body 1. The information acquired by the position sensor, the information acquired by the acceleration sensor, the information acquired by the GPS sensor, and the like may be used to estimate the position and acquire the reliability. The information acquired by the sensor that acquires the information around the moving body 1 may be, for example, the information acquired by the distance measuring sensor, the information acquired by the image sensor, or the like. Hereinafter, the estimation result and the reliability acquired by the first position estimation unit 22 using the sensing result of the first sensor 31 are referred to as the first estimation result and the first reliability, and the second position. The estimation result and reliability acquired by the estimation unit 23 using the sensing result of the second sensor 32 may be referred to as a second estimation result and a second reliability.

Here, the estimation regarding the position and the acquisition of the reliability using the information acquired by the sensor will be described. The estimation regarding the position of the moving body 1 may be, for example, an estimation of the position of the moving body 1 itself (for example, a position in the world coordinate system), and the self-position of the moving body 1 according to the previous estimation result. The amount of movement (difference in position) to be added to may be estimated. In the latter case, for example, when the movement amount is estimated and the position of the moving body 1 is estimated using the estimated movement amount as in the position estimation using the angular position sensor, the acceleration sensor, or the like, the estimation is performed. The movement amount itself may be used, and when the movement amount is not estimated as in the position estimation using a GPS sensor or the like, the movement amount may be obtained by calculating the difference of the estimated self-position. Good.

First, the estimation regarding the position using the information acquired by the distance measuring sensor will be described. The first position estimation unit 22 and / or the second position estimation unit 23 uses an environmental map showing the distance measured by the distance measuring sensor and the position of an obstacle in the moving area of the moving body 1 to move the moving body. You may make an estimate about the position of 1. Strictly speaking, the measured distance is the measured distance in each direction. It is assumed that the environmental map is stored in a recording medium (not shown) accessible to the first position estimation unit 22 and / or the second position estimation unit 23. The obstacle may be an object such as a wall or equipment for which the distance is measured by the distance measuring sensor. The estimated position is usually a position in the world coordinate system. Since self-position estimation using such distance measurement results is already known in SLAM (Simultaneous Localization and Mapping) using distance measurement results, detailed description thereof will be omitted. The first position estimation unit 22 and / or the second position estimation unit 23 may or may not create an environmental map. In the former case, the first position estimation unit 22 and / or the second position estimation unit 23 may be the same as SLAM using the distance measurement result, and in the latter case, the first position estimation unit 23. The position estimation unit 22 and / or the second position estimation unit 23 of the above may perform self-position estimation using an existing environment map.

The reliability of the estimation result regarding the position using the distance measurement result indicates the degree of reliability of the estimation result, that is, the certainty, and is obtained, for example, according to the degree of matching between the measurement result and the environment map. It may be obtained depending on the accuracy of the measurement result, it may be both, or it may be obtained by other methods. Therefore, the reliability is, for example, a match between the measurement result by the first sensor 31 and / or the second sensor 32 and the distance to the obstacle indicated by the environmental map when the moving body 1 is present at the estimated position. The higher the degree of, the larger the value may be. In this case, the reliability may be calculated as follows.
Reliability = (ΣF (Li)) / N1

Here, the function F (x) is a decreasing function that becomes 1 when x = 0 and becomes smaller as x becomes a positive larger value. However, F (x) ≥ 0. Li is the absolute value of the difference between the distance measurement result in the i-th distance measurement direction and the distance to the edge of the obstacle shown by the environmental map. Further, N1 is the total number of i, that is, the total number of distance measurements, and the sum Σ is taken for all i. Since it is standardized by N1, the reliability is a real number of 0 or more and 1 or less. Note that F (x) may be any function, and for example, F (x) = exp (−α × x) may be used. α is a positive real number.

Further, since the error of the distance measurement result by the laser or the like increases due to the influence of reflection or the like as the distance becomes longer, the reliability may be smaller as the distance measurement result is larger, for example. In this case, the reliability may be calculated as follows.
Reliability = (ΣG (Di)) / N1

Here, the function G (x) is a decreasing function that becomes 1 when x = 0 and becomes smaller as x becomes a positive larger value. However, G (x) ≥ 0. Further, Di is the distance measurement result in the i-th distance measurement direction. Further, N1 and the total sum Σ are as described above. Also in this case, since it is standardized by N1, the reliability is a real number of 0 or more and 1 or less. Further, G (x) may be the same as the above function F (x). Further, for example, the reliability increases as the degree of matching between the distance measurement result and the position of the obstacle shown on the environmental map increases, and the value decreases as the distance measurement result increases. Good.

Even when the estimated position cannot be uniquely determined, the reliability may be set to a low value. For example, when the moving body 1 exists in a monotonous environment such as a long corridor, it may not be possible to uniquely determine the estimated position using the distance measurement result. In such a case, the reliability may be set to a low value.

Next, the estimation regarding the position using the information acquired by the image sensor will be described. The first position estimation unit 22 and / or the second position estimation unit 23 correspond to the estimation result and the estimation result by estimating the position of the moving body 1 using the information acquired by the image sensor. You may get the reliability to do. In the estimation of the position, the position may be estimated by using the feature points specified in the captured image acquired by the image sensor and the environment map showing the positions of the feature points in the moving region of the moving body 1. .. It is assumed that the environment map is stored in a recording medium (not shown) accessible to the first position estimation unit 22 and / or the second position estimation unit 23. The estimated position is usually a position in the world coordinate system. Self-position estimation using such a captured image is already known as visual-SLAM, and therefore a detailed description thereof will be omitted. The first position estimation unit 22 and / or the second position estimation unit 23 may or may not create an environmental map. In the former case, the first position estimation unit 22 and / or the second position estimation unit 23 may be the same as the visual-SLAM, and in the latter case, the first position estimation unit 23. The 22 and / or the second position estimation unit 23 may perform self-position estimation using an existing environment map.

The feature points specified in the captured image may be, for example, a predetermined number of FAST key points used in self-position estimation similar to ORB-SLAM, or even SIFT key points, SURF key points, or the like. Good. Further, the reliability indicates the reliability of the estimation result, that is, the certainty, and is acquired according to, for example, the degree of matching between the feature points specified in the captured image and the feature points included in the environmental map. It may be acquired according to the accuracy of the feature points specified in the captured image, both of them may be acquired, or it may be acquired by other methods. Therefore, the reliability is determined by, for example, the feature descriptor (feature amount) of the feature points specified in the captured image acquired by the image sensor and those feature points when the moving body 1 is present at the estimated position. The larger the similarity with the feature descriptor of the feature point included in the corresponding environment map, the larger the value may be. In that case, the reliability may be calculated as follows.
Reliability = (ΣRi) / N2

Here, Ri is the degree of similarity between the feature descriptor of the i-th feature point specified in the captured image and the feature descriptor of the feature point included in the environment map corresponding to the feature point, and is 0 to 1. It is preferable that the values up to are standardized. As for the degree of similarity, the larger the value, the greater the degree of similarity. Further, when the feature point corresponding to the i-th feature point specified in the captured image is not included in the environment map, the similarity may be 0. Further, N2 is the total number of i, that is, the total number of feature points specified in the captured image, and the total Σ is taken for all i. Since it is standardized by N2, the reliability is a real number of 0 or more and 1 or less. When the feature descriptor is a binary vector (for example, in the case of ORB-SLAM), the similarity may be calculated using the Hamming distance. When the feature descriptor is not a binary vector (for example, in the case of SIFT keypoints or SURF keypoints), the similarity may be calculated using the cosine distance or the Euclidean distance.

In addition, the feature points in the captured image cannot be properly identified when the exposure is overexposed and the image is overexposed, or when the exposure is underexposed and the image is blacked out. For example, the larger the evaluation value used in specifying the feature point, the larger the value may be. It is assumed that the candidate of the feature point having a high evaluation value is specified as the feature point. For example, in ORB-SLAM, since a predetermined number of FAST key points are selected from the top of Harris's corner scale, the evaluation value may be Harris's corner scale. That is, the reliability may be a representative value of Harris's corner scale for a plurality of feature points specified in the captured image. The representative value may be, for example, an average value, a median value, a maximum value, or the like. Further, for example, in the identification of feature points (key points) by SIFT, in order to delete low-contrast key point candidates, the size of the extreme value of the key point candidate points is calculated, and the size of the candidate points is less than the threshold value. To delete. Therefore, a representative value of the magnitude of the extremum with respect to the specified plurality of feature points may be used as the reliability. The same applies to other feature points. Further, the reliability is, for example, a value that increases as the degree of similarity between the feature descriptors of the feature points specified in the captured image and the feature descriptors of the feature points included in the environment map corresponding to those feature points increases. At the same time, the larger the evaluation value used in specifying the feature points, the larger the value may be.

For key points, feature points, feature descriptors, etc., refer to the following documents.
Reference 1: Edward Rosten, Tom Drummond, "Machine learning for high-speed corner detection", in 9th European Conference on Computer Vision, vol. 1, 2006, pp. 430-443.
Reference 2: C. Harris, M. Stephens, "A Combined Corner and Edge Detector", In Proceedings of the 4th Alvey Vision Conference (1988), pp. 147-151.
Reference 3: Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary R. Bradski, "ORB: An efficient alternative to SIFT or SURF", ICCV 2011, pp.2564-2571.
Reference 4: D. Lowe, "Distinctive image features from scale-invariant keypoints", Int. Journal of Computer Vision (2004), 60 (2), pp.91-110.
Reference 5: H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding (CVIU) (2008), 110 (3), pp.346- 359.
Reference 6: R. Mur-Artal, JM Montiel, JD Tardos, "ORB-SLAM: A Versatile and Accurate Monocular SLAM System", IEEE Transactions on Robotics 31 (2015), pp.1147-1163.

Next, estimation of the position using the information acquired by the angular position sensor such as an encoder will be described. The amount of rotation of the wheels and the like can be grasped from the amount of rotation and the angle acquired by the angle position sensor. Therefore, the first position estimation unit 22 and / or the second position estimation unit 23 may acquire the estimation result regarding the position by using the information acquired by the angular position sensor. Here, an example of the opposed two-wheel type moving body 1 will be specifically described.

Assuming that the rotation amounts of the left and right wheels acquired by the angular position sensor are Δθ _L and Δθ _R , respectively, and the wheel diameter is R, the movement amounts Δd _L and Δd _R of the left and right wheels are as follows.
Δd _L = RΔθ _L
Δd _R = RΔθ _R

Further, assuming that the distance between the left and right wheels is L, the movement amount Δd and the turning amount Δθ of the moving body 1 can be calculated by the following equations.
Δd = (Δd _R + Δd _L ) / 2
Δθ = (Δd _R − Δd _L ) / L

Assuming that the position of the moving body 1 is (X, Y, Θ), the relative movement amount (ΔX, ΔY, ΔΘ) of the moving body 1 is as follows. Note that (X, Y) is a coordinate value in the two-dimensional XY coordinate system, and Θ may be an angle with reference to the X axis in the two-dimensional coordinate system. Further, the position (X, Y, Θ) of the moving body 1 may be a position in the world coordinate system.
(ΔX, ΔY, ΔΘ) = (Δd × cosΘ, Δd × sinΘ, Δθ)

Therefore, by adding the relative movement amount (ΔX, ΔY, ΔΘ) to the self-position (X _t-1 , Y _t-1 , Θ _t-1 ) of the previous time (time: t-1), the present (time: _t-1 ) The self-position (X _t , Y _t , Θ _t ) of _t ) can be obtained.
(X _t , Y _t , Θ _t ) = (X _t-1 , Y _t-1 , Θ _t-1 ) + (ΔX, ΔY, ΔΘ)

The relative movement amount in the above equation is the movement amount between t-1 and t. The previous self-position may be, for example, one estimated using the information acquired by the angular position sensor, or may be the self-position acquired by the acquisition unit 24 as described later. Further, here, the case where the moving body 1 is of the opposite two-wheel type has been specifically described, but even when the moving body 1 is of another type, similarly, the angular position sensor such as an encoder is used. Needless to say, the acquired information can be used to estimate the position of the moving body 1.

The reliability of the estimation result regarding the position using the information acquired by the angular position sensor is such that the estimation result can be trusted. In position estimation using an angular position sensor, for example, the reliability is lowered due to the influence of the traveling road surface. Specifically, on a wet road surface or a road surface such as gravel, sand, or lawn, the wheels are slippery, and the reliability of position estimation using the corner position sensor is lowered. Therefore, the first position estimation unit 22 and / or the second position estimation unit 23 uses a map showing the area of the slippery road surface of the wheels to show the state of the road surface corresponding to the current position of the moving body 1. (A state indicating whether or not the wheel is slippery) may be specified, and the reliability may be acquired according to the specified result. For example, when the moving body 1 is present on a slippery traveling road surface, low reliability may be acquired, and when it is present on a non-slip traveling road surface, high reliability may be acquired. In this case, for example, reliability may be acquired depending on the degree of slipperiness of the traveling road surface. Specifically, the more slippery the traveling road surface on which the moving body 1 is present, the lower the reliability may be acquired. Further, the reliability in this case is usually the reliability regarding the relative movement amount (ΔX, ΔY, ΔΘ). Further, whether or not the moving body 1 exists on the slippery traveling road surface may be determined using, for example, an estimated position estimated by using the angular position sensor, and the self-position acquired by the acquisition unit 24 may be used. May be judged. In the latter case, for example, the judgment may be made using the previous self-position.

When the current self-position is estimated using only the information acquired by the corner position sensor, low reliability is obtained when there is a slippery road surface on the route to the current self-position. Therefore, high reliability may be obtained when there is no slippery road surface on the route. Further, in this case, for example, reliability may be acquired according to the degree of the slippery traveling road surface included in the route. Specifically, the greater the degree of slippery road surface included in the route, the lower the reliability may be acquired.

Further, it is known that when the position is estimated using only the information acquired by the angular position sensor such as an encoder, the reliability becomes lower as the moving distance becomes longer. This is because the errors are cumulative. Therefore, for example, when the moving body 1 exists in a position where the position is predetermined, the current position estimated by the angular position sensor is adjusted to the position which is determined in advance. The reliability may be acquired according to the moving distance after the alignment. In that case, the longer the moving distance, the lower the reliability will be acquired.

Next, the estimation regarding the position using the information acquired by the acceleration sensor will be described. The acceleration sensor acquires the acceleration of the moving body 1. Then, the first position estimation unit 22 and / or the second position estimation unit 23 can acquire the movement amount (movement distance) by integrating the acquired acceleration in the second order. The current position can be estimated by sequentially adding the travel distances acquired in this way. In this case, the longer the travel distance, the lower the reliability. Further, in this case, since the movement distance can only be acquired, it is necessary to input the initial position. Therefore, for example, the reliability may be acquired according to the moving distance of the moving body 1 from the position where the initial position is set. In addition to the initial position, when the alignment is performed, the reliability may be acquired according to the moving distance of the moving body 1 after the alignment. In this case, the longer the moving distance from the initial position or the moving distance after the alignment, the lower the reliability is acquired.

Further, in estimating the position using the distance measurement result, the photographed image, the amount of rotation of the wheels, the acceleration, etc., for example, a state space model such as a particle filter or a Kalman filter may be used, and the Nelder-Mead method may be used. The sliding simplex method) may be used, or other methods may be used. When a particle filter is used, for example, a representative value of the likelihood of each particle (for example, an average value, a median value, a maximum value, etc.) may be acquired as a reliability according to an estimation result.

Next, position estimation using the information acquired by the GPS sensor will be described. The GPS sensor receives radio waves from a plurality of GPS satellites. Then, the first position estimation unit 22 and / or the second position estimation unit 23 may perform position estimation using the received radio waves from the plurality of GPS satellites. The GPS position estimation is already known, and detailed description thereof will be omitted. The reliability according to the GPS position estimation may be acquired by using, for example, the degree of multipath detected for the received radio wave. In this case, for example, the higher the number of multipaths, the lower the reliability may be acquired. Further, for example, the first position estimation unit 22 and / or the second position estimation unit 23 can use a map showing whether or not radio waves from GPS satellites can be appropriately received at the current position of the moving body 1. The reception state of the corresponding radio wave (the state indicating whether or not the radio wave can be properly received) may be specified, and the reliability may be acquired according to the specific result. For example, when the mobile body 1 exists in an area where radio waves from GPS satellites can be appropriately received, high reliability is acquired and the radio wave reception condition is poor (for example, an area with many multipaths or indoors). If it exists in the area of), low reliability may be obtained. In this case, for example, reliability may be acquired depending on the reception status of radio waves. Specifically, the higher the degree to which the radio waves from the GPS satellites corresponding to the region where the mobile body 1 exists can be received, the higher the reliability may be acquired.

In the estimation of the position using the information acquired by the distance measuring sensor, the image sensor, and the angular position sensor, it is usually possible to estimate the position and posture of the moving body 1. On the other hand, in the estimation of the position using the information acquired by the GPS sensor and the acceleration sensor, usually only the estimation of the position of the moving body 1 is performed. Therefore, when it is necessary to estimate the posture of the moving body 1, the posture may be estimated using the information acquired by the sensor used for detecting the posture such as the gyro sensor or the geomagnetic sensor. For example, when information is acquired by a gyro sensor or a geomagnetic sensor that detects the posture of the moving body 1, the acquired information itself may be used as the posture of the moving body 1. Further, for example, when the angular velocity or angular acceleration of the moving body 1 is acquired by a gyro sensor which is an angular velocity sensor or an angular acceleration sensor, the angle (change amount of posture) or angular acceleration obtained by integrating the angular velocity is obtained. The posture of the moving body 1 may be acquired by integrating the angles (changes in posture) obtained by the second-order integration.

When the posture of the moving body 1 is acquired by using the gyro sensor or the geomagnetic sensor, the reliability of the acquired posture may or may not be acquired. In the former case, it is preferable to obtain the final reliability (position and posture reliability) based on the position reliability and the posture reliability, and in the latter case, it is preferable. The reliability of the position may be used as the reliability of the position and the posture. When the reliability of the position and the posture is generated based on the reliability of the position of the moving body 1 and the reliability of the posture, the reliability of the position and the posture is, for example, the larger the reliability of the position. It may be larger, and the higher the reliability of the posture, the larger the size.

Here, a method of obtaining the reliability of the posture will be briefly described. When acquiring the posture (angle) using the gyro sensor, it is usually necessary to set the initial angle. Therefore, for example, the reliability may be acquired according to the cumulative result of the amount of change in the angle after the initial angle is set. In this case, for example, the higher the cumulative amount of change in the angle from the initial angle setting, the lower the reliability may be acquired. In addition, the geomagnetic sensor usually detects a weak magnetic field to detect the direction. Therefore, when the strength of the magnetic field detected by the geomagnetic sensor is large, it is considered that a magnet exists in the vicinity of the geomagnetic sensor, and the influence of the magnet may increase the directional error. Therefore, for example, the reliability may be acquired according to the magnitude of the strength of the magnetic field detected by the geomagnetic sensor. In this case, for example, the higher the strength of the magnetic field detected by the geomagnetic sensor, the lower the reliability may be acquired.

Further, the reliability acquired by the first and second position estimation units 22 and 23, that is, the first and second reliability, is standardized so that the two can be appropriately compared. Suitable. Specifically, if the first and second reliability values are the same, it is preferable that the first and second estimated positions are standardized so as to have the same reliability. Is. The same applies when three or more reliabilitys are acquired by three or more position estimation units.

The acquisition unit 24 acquires the self-position of the moving body 1 according to the estimation result corresponding to the higher reliability of the first and second reliability. For example, when the first and second estimation results are estimated positions, the acquisition unit 24 sets the estimated position corresponding to the higher reliability of the first and second reliability of the moving body 1. It may be selected as the self-position. Further, for example, when the first and second estimation results are the estimated movement amounts, the acquisition unit 24 relies on the previous self-position with the higher reliance of the first and second reliance. The self-position may be acquired by adding the movement amount corresponding to the degree. In this way, the change in the self-position can be smoothed by adding the movement amount of the higher reliability to the previous self-position. For example, as described above, when selecting the estimated position with higher reliability, it is conceivable that the self-position changes significantly when switching from one estimated position to the other estimated position. On the other hand, when the estimated movement amount is added to the previous self-position, the self-position does not change significantly even if one movement amount is switched to the other movement amount. When acquiring the self-position using the movement amount, it is preferable that the acquisition unit 24 holds the self-position acquired last time. In addition, as described above, when selecting the estimated position with higher reliability, it is possible that the self-position changes significantly. Therefore, when the amount of change exceeds a predetermined threshold value, acquisition is performed. The unit 24 may acquire its own position so as to smoothly connect the two. Further, when the first and second reliabilitys are the same, the acquisition unit 24 may acquire the self-position of the moving body 1 by using one of the estimation results randomly selected, or the previous time. The self-position of the moving body 1 may be acquired by using the estimation result on the same side as the selection of.

Next, the configuration of the moving body 1 other than the self-position estimation device 2 will be described.
The moving mechanism 11 moves the moving body 1. The moving mechanism 11 may or may not be capable of moving the moving body 1 in all directions, for example. Being able to move in all directions means being able to move in any direction. The moving mechanism 11 may have, for example, a traveling unit (for example, a wheel, an endless track, etc.) and a driving means (for example, a motor, an engine, etc.) for driving the traveling unit, and causes the flying object to fly. It may have a flight unit, or may have a navigation unit for navigating a surface navigation body or an underwater navigation body. The flight unit may have, for example, a rotor blade and a driving means for driving the rotor blade. Further, the navigation unit may have, for example, a screw and a driving means for driving the screw. Further, the moving mechanism 11 may be equipped with, for example, a mechanism capable of acquiring the amount of rotation of a wheel or the like, for example, an angular position sensor such as an encoder. When the moving mechanism 11 can move the moving body 1 in all directions, the traveling portion may be an omnidirectional moving wheel (for example, an omni wheel, a mecanum wheel, or the like). Since a known moving mechanism 11 can be used, detailed description thereof will be omitted.

The movement control unit 12 controls the movement of the moving body 1 by controlling the moving mechanism 11 using the self-position acquired by the acquisition unit 24. The movement control may be control such as the movement direction of the moving body 1 and the start / stop of the movement. For example, when a movement route is set, the movement control unit 12 may control the movement mechanism 11 so that the moving body 1 moves along the movement route. More specifically, the movement control unit 12 may control the movement mechanism 11 so that the acquired self-position is along the movement path. Further, the movement control unit 12 may control the movement by using the map. Since the control of the movement mechanism 11 by the movement control unit 12 is known, detailed description thereof will be omitted.

Next, the operation of the self-position estimation device 2 will be described with reference to the flowchart of FIG.
(Step S101) The acquisition unit 24 determines whether or not to acquire the self-position. Then, if the self-position is acquired, the process proceeds to step S102, and if not, the process of step S101 is repeated until it is determined that the self-position is acquired. The acquisition unit 24 may periodically determine that the self-position is acquired, for example.

(Step S102) The first and second sensors 31 and 32 each acquire information used for estimating the position.

(Step S103) The first position estimation unit 22 acquires the first estimation result regarding the position of the moving body 1 by using the information acquired by the first sensor 31, and responds to the first estimation result. Get reliability.

(Step S104) The second position estimation unit 23 acquires the second estimation result regarding the position of the moving body 1 by using the information acquired by the second sensor 32, and responds to the second estimation result. Get reliability. It is preferable that the acquisition of the first and second estimation results is performed synchronously.

(Step S105) The acquisition unit 24 acquires the self-position using the estimation result corresponding to the higher reliability of the first and second reliability. Then, the process returns to step S101.

Although the flowchart of FIG. 2 shows only the process of acquiring the self-position, in the moving body 1, the movement control unit 12 controls the movement using the acquired self-position, and the control thereof is performed. The process of movement according to the above may be performed by the movement mechanism 11. Further, the order of processing in the flowchart of FIG. 2 is an example, and the order of each step may be changed as long as the same result can be obtained. Further, in the flowchart of FIG. 2, the processing is terminated by an interrupt of power off or processing termination.

Next, the operation of the moving body 1 according to the present embodiment will be described with reference to a simple specific example. In this specific example, it is assumed that the moving body 1 is used as a transfer robot in the factory. Further, it is assumed that the first sensor 31 is an encoder that acquires the amount of rotation of the wheel, and the second sensor 32 is a laser range sensor. Further, the first position estimation unit 22 shall acquire the reliability by using a map showing a slippery region of the wheel. Further, the first and second position estimation units 22 and 23 estimate the amount of movement to be added to the previous self-position, and the acquisition unit 24 has a higher reliability than the previous self-position. The current self-position shall be acquired by adding the amount of movement.

First, it is assumed that the moving body 1 is moving in a region where there is a lot of traffic and the wheels are not slippery. In such a situation, the rotation amount of the wheel is measured by the first sensor 31, the movement amount which is the first estimation result is acquired according to the measurement result, and the first reliability regarding the movement amount is obtained. Suppose it was acquired. Further, the distance to the obstacle is measured by the second sensor 32, the movement amount which is the second estimation result is acquired according to the measurement result, and the second reliability regarding the movement amount is acquired. (Steps S101 to S104). In this case, the first reliability is a high value. This is because the moving body 1 does not exist in the slippery region of the wheel. On the other hand, the second reliability is a low value. This is because there are many obstacles (humans) that are not included in the environmental map. Therefore, the acquisition unit 24 newly selects the movement amount, which is the first estimation result corresponding to the first reliability, which is a higher value, and adds the selected movement amount to the previous self-position. The self-position is acquired and passed to the movement control unit 12 (step S105). Then, the movement is controlled according to the self-position.

After that, it is assumed that the moving body 1 moves to an area where there is no traffic and the wheels are slippery. In such a situation, the information is acquired by the first and second sensors 31, 32, and the first and second position estimation units 22, 23, the first and second estimation results, and the first and second estimation results. It is assumed that the reliability of 2 is acquired (steps S101 to S104). In this case, the first reliability is a low value. This is because the moving body 1 exists in the slippery region of the wheel. On the other hand, the second reliability is a high value. This is because there are no obstacles (humans) that are not included in the environmental map. Therefore, the acquisition unit 24 newly selects the movement amount, which is the second estimation result corresponding to the second reliability, which is a higher value, and adds the selected movement amount to the previous self-position. The self-position is acquired and passed to the movement control unit 12 (step S105). Then, the movement is controlled according to the self-position.

As described above, according to the moving body 1 according to the present embodiment, the self-position of the moving body 1 is acquired by using the estimation result corresponding to the higher reliability of the first and second reliability. Therefore, it is possible to acquire the self-position with higher accuracy. Further, when the first and second position estimation units 22 and 23 estimate the movement amount, the current self-position can be acquired by adding the estimated movement amount to the previous self-position. Even if the movement amount used for acquiring the self-position is switched according to the reliability, it is possible to avoid a large change in the self-position, and it is possible to realize smooth acquisition of the self-position.

In the present embodiment, the case where the sensor unit 21 has the first and second sensors 31 and 32 has been mainly described, but it may not be the case. As shown in FIG. 3, the sensor unit 21 may have only one sensor 33. In this case, the first and second position estimation units 22 and 23 use the same information acquired by the sensor 33 of the sensor unit 21 to estimate the position of the moving body 1 by different methods. May be good. For example, when the position is estimated by using a state space model such as a particle filter or a Kalman filter, or the Nelder-Mead method, the first and second position estimation units 22 and 23 relate to the position by different models and methods. Estimates may be made. Specifically, when the sensor 33 is a distance measuring sensor, the first position estimation unit 22 estimates the position using a particle filter, and the second position estimation unit 23 uses a Kalman filter to estimate the position. May make an estimate. Further, when the first and second position estimation units 22 and 23 perform position estimation by the same model and the same method, the first and second position estimation units 22 and 23 have different settings (parameters). Position estimation may be performed by a model or method. For example, when the first and second position estimation units 22 and 23 use particle filters, the number of particles may be different from each other. Further, for example, when the first and second position estimation units 22 and 23 use the Nelder-Mead method, the number of vertices of the simplex may be different, and the initial positions of the vertices of the simplex may be different. Good. Even when the sensor unit 21 has two sensors that acquire the same information, the positions of the moving body 1 are similarly determined by the first and second position estimation units 22 and 23 by different methods. May make an estimate.

Further, it is considered difficult for the first and second position estimation units 22 and 23 to estimate the position by different methods using the information acquired by the GPS sensor. It is also considered difficult to acquire different information depending on the sensor by the two GPSs. Therefore, when a GPS sensor is provided, the sensor unit 21 also has a sensor of a type other than the GPS sensor, and one of the first and second position estimation units 22 and 23 has a position estimation unit. It is preferable that the position is estimated using the information acquired by the GPS sensor, and the other position estimation unit estimates the position using the information acquired by the sensor other than the GPS sensor.

Further, in the present embodiment, the first position estimation unit 22 and / or the second position estimation unit 23 may estimate the self-position by using a plurality of information capable of estimating the self-position. For example, the first position estimation unit 22 and / or the second position estimation unit 23 uses the distance measurement result acquired by the distance measurement sensor and the rotation amount and angle of the drive shaft acquired by the angle position sensor. You may estimate your own position. In this case, for example, a state space model such as a particle filter may be used. Further, in this case, the sensor unit 21 may include, for example, two or more types of sensors, for example, a distance measuring sensor and an angular position sensor. Further, in this case, the reliability corresponding to the likelihood of the particle filter or the like may be acquired.

Further, in the present embodiment, as described above, the self-position estimation device 2 may include three or more position estimation units. In this case, the acquisition unit 14 may acquire the self-position of the moving body 1 according to the estimation result corresponding to the highest reliability among the plurality of reliabilitys acquired by the plurality of position estimation units. Good. Further, in this case, the sensor unit 21 may have the same number of sensors as the position estimation unit. Then, the plurality of position estimation units may estimate the position of the moving body 1 by using the plurality of information acquired by the plurality of sensors. Further, as described above, when the sensor unit 21 has one sensor 33, the plurality of position estimation units use the same information acquired by the sensor unit 21 and use different methods for the moving body 1 You may make an estimate about the position of. When three or more position estimation units estimate the position of the moving body 1, the acquisition unit 14 sets the estimated position corresponding to the highest reliability among the plurality of reliabilitys to the self-position of the moving body 1. May be selected as. Further, when three or more position estimation units estimate the amount of movement to be added to the previous self-position, the acquisition unit 14 has the highest reliability among the plurality of reliabilitys at the previous self-position. The self-position may be acquired by adding the movement amount corresponding to. Further, in this case, the sensor unit 21 may have a plurality of sensors having a smaller number than the position estimation unit. For example, when the self-position estimation device 2 includes N position estimation units, the sensor unit 21 may have K sensors that acquire different information. It is assumed that N is an integer of 3 or more, and K is an integer satisfying 1 <K <N. Then, the K position estimation units estimate the position of the moving body 1 by using the plurality of information acquired by the K sensors, respectively, and the remaining (NK) position estimation units are K. The position of the moving body 1 may be estimated using the same information as the position estimation units. In this case, the two or more position estimation units that estimate the position of the moving body 1 using the information of a certain sensor may estimate the position of the moving body 1 by different methods.

Further, when the self-position acquired by the self-position estimation device 2 is also used in another device or the like, the self-position estimation device 2 outputs an output unit that outputs the self-position acquired by the acquisition unit 24. You may have. The output may be, for example, transmitted to a predetermined device via a communication line or stored in a recording medium. The output unit may or may not include a device that outputs (for example, a communication device). Further, the output unit may be realized by hardware, or may be realized by software such as a driver that drives those devices.

Further, in the above embodiment, each process or each function may be realized by centralized processing by a single device or a single system, or distributed processing by a plurality of devices or a plurality of systems. It may be realized by.

Further, in the above embodiment, the transfer of information performed between the respective components depends on, for example, one of the components when the two components that transfer the information are physically different. It may be performed by outputting information and accepting information by the other component, or if the two components that pass the information are physically the same, one component. It may be performed by moving from the processing phase corresponding to the above to the processing phase corresponding to the other component.

Further, in the above embodiment, information related to the processing executed by each component, for example, information received, acquired, selected, generated, transmitted, or received by each component. In addition, information such as threshold values, mathematical formulas, and addresses used by each component in processing may be temporarily or for a long period of time in a recording medium (not shown) even if it is not specified in the above description. In addition, each component or a storage unit (not shown) may store information on a recording medium (not shown). Further, the information may be read from the recording medium (not shown) by each component or a reading unit (not shown).

Further, in the above embodiment, when the information used in each component or the like, for example, the information such as the threshold value and the address used in the processing by each component and various setting values may be changed by the user, the above The information may or may not be changed as appropriate by the user, even if not specified in the description. When the information can be changed by the user, the change is realized by, for example, a reception unit (not shown) that receives a change instruction from the user and a change unit (not shown) that changes the information in response to the change instruction. You may. The reception unit (not shown) may accept the change instruction from, for example, an input device, information transmitted via a communication line, or information read from a predetermined recording medium. ..

Further, in the above embodiment, when two or more components included in the self-position estimation device 2 have a communication device, an input device, or the like, the two or more components have a physically single device. It may also have separate devices.

Further, in the above embodiment, each component may be configured by dedicated hardware, or a component that can be realized by software may be realized by executing a program. For example, each component can be realized by a program execution unit such as a CPU reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory. At the time of execution, the program execution unit may execute the program while accessing the storage unit or the recording medium. Further, the program may be executed by being downloaded from a server or the like, or may be executed by reading a program recorded on a predetermined recording medium (for example, an optical disk, a magnetic disk, a semiconductor memory, etc.). Good. Further, this program may be used as a program constituting a program product. Further, the number of computers that execute the program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

Further, it goes without saying that the present invention is not limited to the above embodiments, and various modifications can be made, and these are also included in the scope of the present invention.

From the above, the self-position estimation device according to the present invention has the effect of being able to acquire a self-position with high accuracy, and is useful as, for example, a self-position estimation device used in a moving body that moves autonomously.

1 mobile body, 2 self-position estimation device, 21 sensor unit, 22 first position estimation unit, 23 second position estimation unit, 24 acquisition unit, 31 first sensor, 32 second sensor

Claims (5)

It is a self-position estimation device that estimates the self-position of a moving body.
A sensor unit that acquires information used for estimating the position of the moving body, and
A plurality of position estimation units that estimate the position of the moving body using the information acquired by the sensor unit and acquire the reliability of the estimation result, and
A self-position estimation device including an acquisition unit that acquires the self-position of the moving body according to an estimation result corresponding to the highest reliability among a plurality of reliabilitys acquired by the plurality of position estimation units. .. The sensor unit has a plurality of sensors that are sensors that acquire different information.
The self-position estimation device according to claim 1, wherein the plurality of position estimation units estimate the position of the moving body by using a plurality of information acquired by the plurality of sensors. The self-position estimation device according to claim 1, wherein the plurality of position estimation units use the same information acquired by the sensor unit to estimate the position of the moving body by different methods. The plurality of position estimation units estimate the position of the moving body.
The self-position estimation device according to any one of claims 1 to 3, wherein the acquisition unit selects an estimated position corresponding to the highest reliability among the plurality of reliabilitys as the self-position of the moving body. The plurality of position estimation units estimate the amount of movement to be added to the previous self-position.
The method according to any one of claims 1 to 3, wherein the acquisition unit acquires the self-position by adding the movement amount corresponding to the highest reliability among the plurality of reliabilitys to the previous self-position. Self-position estimation device.

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