Information processing device, information processing method, mobile body, program, and system

Abstract

This information processing device comprises: an acquisition unit that acquires observation data obtained by a sensor that observes a target; a determination unit that determines the geographical position of the target using the observation data; and a filter unit that applies a filter for reducing the influence of observation noise to time-series data of the geographical position. The filter unit determines the strength of the filter on the basis of the distance between a reference position associated with the sensor and the geographical position of the target.

Description

Information Processing Apparatus, Information Processing Method, Mobile Body, Program, and System

[0001] The present invention relates to an information processing apparatus, an information processing method, a mobile body, a program, and a system.

[0002] In order to assist a user, an autonomous mobile robot capable of following the user is provided. In the technology described in Patent Document 1, a virtual following target different from the following target is set in order to make the autonomous mobile robot follow the user with more natural behavior.

[0003] Japanese Unexamined Patent Application Publication No. 2021-77088

[0004] In order to move a mobile body safely, it is important to accurately detect the geographical position of a target around the mobile body. Some aspects of the present invention provide a technique for accurately detecting the geographical position of a target.

[0005] According to some embodiments, there is provided an information processing apparatus including: an acquisition unit that acquires observation data obtained by a sensor that observes a target; a determination unit that determines the geographical position of the target using the observation data; and a filter unit that applies a filter for reducing the influence of observation noise to time-series data of the geographical position, wherein the filter unit determines the strength of the filter based on a distance between a reference position related to the sensor and the geographical position of the target.

[0006] According to some embodiments, the geographical position of a target can be accurately detected.

[0007] Other features and advantages of the present invention will become apparent from the following description with reference to the accompanying drawings. In the accompanying drawings, the same or similar components are denoted by the same reference numerals.

[0008] The attached drawings are included in the specification and constitute part thereof, illustrating embodiments of the present invention and are used to explain the principles of the present invention together with the description thereof. Schematic diagram illustrating an example of the external configuration of a mobile body in some embodiments. Block diagram illustrating an example of the functional configuration of a mobile body in some embodiments. Flowchart illustrating a control method for a mobile body in some embodiments. Schematic diagram illustrating a method for determining the geographical position of a dynamic target in some embodiments. Schematic diagram illustrating a method for determining the distance to a dynamic target in some embodiments.

[0009] The embodiments will be described in detail below with reference to the attached drawings. Note that the following embodiments do not limit the invention as defined in the claims, and not all combinations of features described in the embodiments are essential to the invention. Two or more of the features described in the embodiments may be combined in any way. Furthermore, identical or similar configurations will be given the same reference numeral, and redundant descriptions will be omitted.

[0010] <Configuration of the Mobile Unit> Referring to Figure 1, an example of the external configuration of the mobile unit 100 according to one embodiment will be described. In Figure 1, arrow X indicates the front-rear direction of the mobile unit 100. F indicates the front and R indicates the rear. Arrows Y and Z indicate the width direction (left-right direction) and the up-down direction of the mobile unit 100, respectively. The mobile unit 100 is capable of autonomous movement. For example, the mobile unit 100 is equipped with a battery and moves mainly by motor power. The mobile unit 100 may be used on the premises of amusement facilities, large commercial facilities, airports, parks, sidewalks, parking lots, etc. The mobile unit 100 may be a vehicle that moves on the ground using wheels, an aircraft that moves in the air (e.g., a drone), or a robot that moves on the ground using two or more legs. Movement using wheels is also called driving. Movement in the air is also called flying. Movement using two or more legs is also called walking. The following description will focus on the case where the mobile unit 100 is a vehicle, but similar descriptions apply to other forms of mobile units.

[0011] The mobile unit 100 is capable of moving in accordance with the user of the mobile unit 100 (hereinafter simply referred to as the user). Moving in accordance with the user means that the mobile unit 100 moves based on the user's proactive movements. In the following example, a configuration in which no person rides in the mobile unit 100 is described, but the mobile unit 100 may carry a person other than the user. The mobile unit 100 includes, for example, a pair of left and right front wheels 101 and rear wheels 102, which are included in the driving unit 204 (Figure 2). The driving unit 204 may take other forms, such as a four-wheeled vehicle or a two-wheeled vehicle. The mobile unit 100 may be capable of moving autonomously to a pre-set destination instead of, or in addition to, the user of the mobile unit 100 accompanying it.

[0012] The mobile unit 100 has a housing 110 capable of accommodating luggage. The front 111 of the housing is provided with a lid that can be opened and closed to accommodate luggage, and the lid is equipped with a locking mechanism. The locking mechanism is controlled by the mobile unit 100. For example, the mobile unit 100 releases the lock when it successfully authenticates the user. Alternatively, the mobile unit 100 may accommodate luggage in other ways.

[0013] A touchscreen 120 is located on the top surface 112 of the housing, allowing the user to, for example, change the settings of the mobile unit 100 or check information about the facility. The sensor box 130 contains a detection unit 206 (Figure 2), such as a camera, inside. The detection unit 206 recognizes the user and other targets included in the environment surrounding the mobile unit 100 through the front 131, sides, and back of the sensor box 130.

[0014] <Example of Functional Configuration of Mobile Body> An example of the functional configuration of the mobile body 100 will be described with reference to Figure 2. The mobile body 100 includes the components shown in Figure 2. The mobile body 100 may also include components not shown in Figure 2, or may not include some of the components shown in Figure 2.

[0015] The mobile unit 100 is an electrically powered autonomous mobile unit equipped with a driving unit 204 and powered primarily by a battery 205. The battery 205 is a secondary battery such as a lithium-ion battery, and the driving unit 204 uses the power supplied from the battery 205 to propel the mobile unit 100.

[0016] The driving unit 204 accelerates and decelerates the mobile body 100 by changing the rotational speed of a pair of front wheels 101 using a motor as the drive source. The driving unit 204 may also include a braking mechanism for decelerating the mobile body 100. The driving unit 204 steers the mobile body 100 by making the rotational speeds of the pair of front wheels 101 different. The driving unit 204 can detect and output physical quantities representing the motion of the mobile body 100, such as the mobile body 100's speed, acceleration, steering angle, angular velocity of the mobile body 100's housing 110, and angular acceleration.

[0017] The mobile body 100 includes a detection unit 206 which contains one or more sensors. The detection unit 206 generates data for recognizing targets (including objects and people in the environment surrounding the mobile body 100) included in the environment surrounding the mobile body 100. The detection unit 206 includes sensors such as an imaging device (camera), radar device, lidar (Light Detection and Ranging), and ultrasonic sensor, which have a detection range around the mobile body 100, and outputs sensor information. The imaging device may be configured to use a fisheye lens or to be configured to perform stereo imaging. Furthermore, the detection unit 206 includes a GNSS (Global Navigation Satellite system) sensor and receives GNSS signals to detect the current position of the mobile body 100. The detection unit 206 may also detect the current position using wireless LAN (Local Area Network) or Bluetooth signals. The imaging device may be an RGB camera or may further have a depth measurement function. For example, the mobile unit 100 may have RGB cameras with depth measurement capabilities on the front and rear sides of the sensor box 130, and RGB cameras without depth measurement capabilities on the right and left sides of the sensor box 130.

[0018] The mobile unit 100 includes a control unit (ECU (Electronic Control Unit)) 201. The control unit 201 functions as a control device for the mobile unit 100. The control unit 201 includes one or more processors 202, such as a CPU (Central Processing Unit), and a memory 203, which is a storage device such as a semiconductor memory. Therefore, the control unit 201 can also be called an information processing device or a computer. The memory 203 stores programs executed by the processors 202 and data used by the processors 202 for processing. Multiple sets of processors 202 and memory 203 may be provided according to the function of the mobile unit 100 and configured to communicate with each other.

[0019] The control unit 201 acquires physical quantities representing the motion output by the travel unit 204, detection results from the detection unit 206, input information from the touchscreen 120, and audio information input from the audio input device 207, and executes corresponding processing. For example, the control unit 201 controls the motor of the travel unit 204, controls the display on the touchscreen 120, and provides audio notifications to the surrounding environment.

[0020] The voice input device 207 picks up sounds from the environment surrounding the mobile body 100. The control unit 201 can recognize the input sounds and execute corresponding processing. The storage device 208 is a non-volatile large-capacity storage device that stores map information including roads that the mobile body 100 can travel on, areas where entry is restricted, landmarks, stores, etc. The storage device 208 may also store programs executed by the processor 202 and data used by the processor 202 for processing. The communication device 209 is a communication device that can connect to an external network via wireless communication such as fifth-generation mobile communication or wireless LAN.

[0021] The presentation device 210 displays (presents) a user interface screen to the user on the touchscreen 120, or outputs (presents) spoken audio to the environment surrounding the mobile body 100 via the microphone. The input device 211 may include, for example, a touch panel and be configured as an integral part of the touchscreen 120. The input device 211 receives operation input from the user via the touch panel.

[0022] The processor 202 of the control unit 201 implements the functions of the information acquisition unit 221, the target determination unit 222, and the driving control unit 223 by executing a program stored in the memory 203 or the storage device 208. The information acquisition unit 221 acquires various information used for processing by other components. The target determination unit 222 determines the target position of the mobile body 100 for the user based on the information acquired by the information acquisition unit 221. The target position is the target position to which the mobile body 100 will move. The driving control unit 223 supplies control signals to the driving unit 204 to move the mobile body 100. The driving control unit 223 moves the mobile body 100 toward the target position. Details of the processing of the information acquisition unit 221, the target determination unit 222, and the driving control unit 223 will be described later.

[0023] <Method for controlling the mobile body 100> Referring to Figure 3, a method by which the control unit 201 controls the mobile body 100 will be described. As described above, the control unit 201 can control the mobile body 100 to accompany the user. Each step of the method in Figure 3 may be performed by the processor 202 executing a program stored in memory 203 or storage device 208. Alternatively, at least some of the steps of the method in Figure 3 may be performed by a dedicated integrated circuit such as an ASIC (Application Specific Integrated Circuit). The method in Figure 3 may be started in response to the user instructing the mobile body 100 to accompany the user. The control unit 201 repeatedly executes the processes S301 to S303 (for example, in 100 ms cycles).

[0024] The method shown in Figure 3 is performed on a user of the mobile device 100. At the start of the method in Figure 3, the control unit 201 identifies the user and controls the mobile device 100 to accompany this user. The control unit 201 may identify a person positioned in front of the mobile device 100 in an image captured by the detection unit 206 (for example, an imaging device) or a person who has previously registered as a user as the user. During the execution of the method in Figure 3, the control unit 201 continuously determines whether it can detect the user. If the user cannot be detected for a predetermined period of time since the last detection, the control unit 201 determines that the user has been lost and interrupts the method in Figure 3. After that, the control unit 201 may restart the method in Figure 3 depending on whether the user has been detected.

[0025] In S301, the control unit 201 (for example, the information acquisition unit 221) acquires information to be used in subsequent processing. This information may include user information, mobile device information, and environmental information. The control unit 201 may store at least a portion of the acquired information in the memory 203 or storage device 208 for use in subsequent processing.

[0026] User information refers to information about the user. User information may include the user's current geographical location and the user's current posture. The control unit 201 may acquire user information based on the detection results of the detection unit 206 (for example, images captured by the imaging device).

[0027] Mobile object information refers to information about the mobile object 100. Mobile object information may include the current speed of the mobile object 100, the current geographical location of the mobile object 100, and the current angular velocity of the mobile object 100. The control unit 201 may acquire mobile object information based on the output from the driving unit 204 or the detection results from the detection unit 206 (for example, GNSS positioning data or inertial sensor data).

[0028] Environmental information refers to information about the environment surrounding the mobile body 100. Environmental information may include the number, type, location, and size of targets included in the environment surrounding the mobile body 100. Targets may include static targets, semi-static targets, and dynamic targets. Static targets may include structures such as walls, guardrails, pillars, and steps. Static targets are targets that are fixed directly or indirectly to the ground and do not move. Static targets that can obstruct the movement of the mobile body 100 may be called static obstacles. Semi-static targets may include desks, chairs, and stand signs. Semi-static targets are targets that can be moved by people but do not actively move themselves. Semi-static targets that can obstruct the movement of the mobile body 100 may be called semi-static obstacles. Dynamic targets may include pedestrians, cyclists, autonomous mobile bodies, animals, etc. Dynamic targets are targets that can move relative to the ground. Among the dynamic targets, those that can obstruct the movement of the moving body 100 may be called dynamic obstacles. The control unit 201 may acquire environmental information based on the detection results of the detection unit 206 (for example, images captured by the imaging device).

[0029] In S302, the control unit 201 (for example, the target determination unit 222) determines the target position of the moving body 100. For example, the control unit 201 may first predict the direction of movement in which the user is trying to move, based on various information acquired in S301. The direction of movement of the user may be predicted based on, for example, the orientation of the user's body or the user's past movement path. The control unit 201 may then determine a position that forms a predetermined angle with respect to the predicted movement path and is at a predetermined distance from the user as the target position. The control unit 201 may determine the target position so as not to overlap with obstacles (for example, static obstacles).

[0030] In S303, the control unit 201 (for example, the driving control unit 223) moves the mobile body 100 toward the target position determined in S302. Specifically, the control unit 201 generates a trajectory from the current position of the mobile body 100 toward the target position and moves the mobile body 100 along this trajectory. Depending on the current attitude and speed of the mobile body 100, this trajectory may or may not pass through the target position. After that, the control unit 201 transitions to processing S301 and repeats the above processing.

[0031] While executing the method shown in Figure 3, the control unit 201 may monitor the distance between the moving body 100 and any dynamic obstacles around it, and if this distance falls within a threshold (e.g., 1.5 m), it may take action (e.g., make an audio announcement) to ask the dynamic obstacles to yield the path. Furthermore, while executing the method shown in Figure 3, the control unit 201 may stop the moving body 100 if the distance between the moving body 100 and any dynamic obstacles around it falls within another threshold (e.g., 1 m). In addition, in S303, the control unit 201 may generate a trajectory that avoids the dynamic obstacles.

[0032] <Method for acquiring the position of a dynamic target> Referring to Figure 4, a specific example of the method for acquiring the geographical position of a dynamic target in S301 of Figure 3 will be described. The dynamic target may be the user of the mobile body 100, or it may be another dynamic target included in the environment surrounding the mobile body 100. As described above, S301 is executed repeatedly, and the geographical position of the dynamic target is acquired each time S301 is executed. The method in Figure 4 is executed each time S301 is executed. If the environment surrounding the mobile body 100 includes multiple dynamic targets, the control unit 201 may execute the method in Figure 4 for each dynamic target.

[0033] In S401, the control unit 201 (for example, the information acquisition unit 221) acquires the observation data obtained by the detection unit 206. The detection unit 206 observes dynamic targets included in the environment surrounding the moving body 100. The observation data may include images taken by an imaging device (camera), which is an example of the detection unit 206. Alternatively or in addition to this, the observation data may include data observed by a lidar, which is an example of the detection unit 206. The data observed by the lidar may include the direction of the target and the distance to the target.

[0034] In S402, the control unit 201 (for example, the information acquisition unit 221) determines the geographical position of the moving target using the observation data acquired in S401. The geographical position may be represented by two-dimensional coordinate values ​​in the horizontal plane. The geographical position may be defined by the relative position of the moving object 100 to the geographical position at the time the method in Figure 4 was started, or by latitude and longitude.

[0035] For example, the control unit 201 may determine the geographic location by homographic transformation of images captured by the imaging device (camera). Alternatively, or in addition to this, the control unit 201 may determine the geographic location based on data observed by the LiDAR (i.e., the direction of the target and the distance to the target).

[0036] The observation data acquired in S401 includes observation noise. Observation noise is noise that is mixed in when the sensor (e.g., camera or lidar) included in the detection unit 206 observes the target. In S403, the control unit 201 (e.g., the information acquisition unit 221) applies a filter to the time-series data of the geographical location determined in S402 to reduce the effect of observation noise. A Kalman filter, a particle filter, a low-pass filter, or any other filter may be used as the filter to reduce the effect of observation noise.

[0037] In S404, the control unit 201 (for example, the information acquisition unit 221) stores the geographical location of the target after the filter has been applied in S403 in the memory 203 or storage device 208 for use in subsequent processing. This geographical location of the target may be used in S302 and S303 described above. Alternatively, this stored geographical location may be used as time-series data of geographical location to be used in S403, which is executed in a subsequent repetition of S301. After that, the control unit 201 terminates processing.

[0038] <Characteristics of Observation Noise and Filter Strength> The influence of observation noise on determining the geographical location of a target may vary depending on the method used to determine the target's geographical location. The influence of observation noise on the method of determining the geographical location of a target by homographic transformation of an image captured by a camera will be explained below with reference to Figure 5. Figure 5 schematically shows the relationship between an image 502 captured by a camera 501 mounted on a mobile body 100 and a target 503. The position where the target 503 (for example, a dynamic target such as a person) is in contact with the horizontal plane 504 (for example, the ground) is represented as the actual position 505 of the target 503. The actual position 505 is the actual geographical location of the target 503. For simplicity of explanation, it is assumed that the actual position 505 lies on a plane that passes through the optical axis of the camera 501 and is perpendicular to the horizontal plane 504. The actual position 505 corresponds to position 506 in image 502. If image 502 is free of observation noise and position 506 can be identified, the control unit 201 can determine the actual position 505 of the target 503 by performing a homographic transformation on position 506. However, due to the influence of observation noise during observation by the camera 501, the actual position 505 of the target 503 may be observed in image 502 as a position 507 that is shifted from position 506. In this case, the control unit 201 determines the geographic position obtained by performing a homographic transformation on position 507 as the observed position 508 of the target 503. The observed position 508 is the geographic position of the target 503 determined using the observation data (image 502 in the example of Figure 5). The observed position 508 may differ from the actual position 505.

[0039] Let the distance between the center 509 of the image 502 and the position 507 be d, the focal length of the camera 501 be f, and the height of the camera 501 from the horizontal plane 504 be h. The focal length f is a value specific to the camera 501, and the height h is a value determined according to the mounting position of the camera 501 on the moving body 100. Also, let the position where the camera 501 is projected onto the horizontal plane 504 be the reference position 510 related to the camera 501. Further, let the distance between the reference position 510 and the observation position 508 be L. The distance L is given by L = h×f / d …(1).

[0040] Furthermore, let the distance between the position 506 and the position 507 be Δd, and the distance between the actual position 505 and the observation position 508 be ΔL. ΔL is given by ΔL = - {L ２ / (h×f)}Δd …(2).

[0041] Therefore, the observation noise during observation by the camera 501 affects the determination of the geographical position of the target 503 at the second - order of the distance L. Thus, even if the magnitude of the observation noise (Δd) is the same value, the larger the distance L, the larger the deviation (ΔL) of the observation position 508 from the actual position 505.

[0042] Therefore, in the above - mentioned S403, the control unit 201 determines the strength of the filter based on the distance L between the reference position 510 and the observation position 508. For example, when the image 502 of the camera 501 is used as described above, the control unit 201 determines the strength of the filter such that the strength of the filter increases as the distance L between the reference position 510 and the observation position 508 increases. The strength of the filter represents the degree of dependence on the current observation position 508 when determining the current geographical position of the target 503 based on the past geographical position of the target 503 and the current observation position 508 of the target 503. The higher the strength of the filter, the lower the degree of dependence on the current observation position 508. On the other hand, the lower the strength of the filter, the higher the degree of dependence on the current observation position 508.

[0043] As described above, when the geographical location of the target 503 is determined using the image 502 from camera 501, if the distance L between the reference position 510 and the observation position 508 is large, the influence of observation noise on the determination of the geographical location is large. Therefore, by increasing the strength of the filter, the influence of observation noise can be reduced, and the likelihood that the geographical location of the target 503 after applying the filter will be close to the current actual position 505 increases. On the other hand, if the distance L between the reference position 510 and the observation position 508 is small, the influence of observation noise on the determination of the geographical location is small. Therefore, by lowering the strength of the filter, the contribution to the highly accurate observation position 508 increases, and the likelihood that the geographical location of the target 503 after applying the filter will be close to the current actual position 505 increases. In this way, by determining the strength of the filter based on the distance L between the reference position 510 and the observation position 508, the geographical location of the target 503 can be determined with high accuracy.

[0044] In the example described above, the larger the distance L, the greater the influence of observation noise during observation on determining the geographical position of the target 503. Therefore, the control unit 201 determines the filter strength such that the filter strength increases as the distance L between the reference position 510 and the observation position 508 increases. Alternatively, if the geographical position of the target is determined in a way that the influence of observation noise during observation on determining the geographical position of the target 503 decreases as the distance L increases, the control unit 201 may determine the filter strength such that the filter strength decreases as the distance L between the reference position 510 and the observation position 508 increases.

[0045] Next, we will explain how to determine the filter strength when a Kalman filter is used in S403. In a Kalman filter, the filter strength is determined by the Kalman gain. Therefore, the control unit 201 determines the Kalman gain based on the distance L between the reference position 510 and the observation position 508.

[0046] The Kalman gain K is given by K = PH Ｔ (HPH Ｔ +R) －１... given by (3). Here, P is the covariance matrix of the prior distribution of the state of the target 503. The state of the target 503 includes the geographical position of the target 503 (i.e., two-dimensional coordinate values). The state of the target 503 may include other states of the target 503 (e.g., velocity). H is the observation function. R is the covariance matrix representing the magnitude of the observation noise.

[0047] Determining the strength of the filter may include determining the magnitude of the observation noise (i.e., at least any component of the covariance matrix R) used in the Kalman filter based on the distance L between the reference position 510 and the observation position 508. For example, determining the magnitude of the observation noise used in the Kalman filter may include determining the diagonal components of the covariance matrix R based on the distance L between the reference position 510 and the observation position 508. For example, the control unit 201 may determine the diagonal components of the covariance matrix R to increase as the distance L increases. The control unit 201 may determine the off-diagonal components of the covariance matrix R without depending on the distance L. For example, the control unit 201 may determine the off-diagonal components of the covariance matrix R according to a multivariate normal distribution having a predetermined variance.

[0048] As described above, the observation noise during observation by the camera 501 affects the determination of the geographical position of the target 503 in the order of the second power of the distance L. Therefore, the control unit 201 may determine the diagonal components of the covariance matrix R such that the diagonal components of the covariance matrix R increase in the order of the second power of the distance L. Specifically, the control unit 201 may use the value obtained by the quadratic function of the distance L as the diagonal components of the covariance matrix R. This quadratic function may also be called a position error model. The coefficients of the quadratic function are determined in advance to represent the variance of the observation position 508 at the distance L. Instead of the quadratic function of the distance L, other functions of the distance L (e.g., linear function) may be used.

[0049] Instead of determining the filter strength by determining the magnitude of the observation noise, or in addition to this, the filter strength may be determined by multiplying the Kalman gain K by a coefficient based on the distance L. For example, the control unit 201 may multiply the Kalman gain K by a coefficient that decreases with respect to the distance L. As a result, the filter strength decreases as the distance L between the reference position 510 and the observation position 508 increases.

[0050] Next, a method for determining the filter strength when a particle filter is used in S403 will be described. Determining the filter strength may include determining the magnitude of the observation noise used in the particle filter based on the distance L between the reference position 510 and the observation position 508. In the particle filter, as with the Kalman filter, the magnitude of the observation noise is represented by the covariance matrix. Therefore, the control unit 201 may determine the components of the covariance matrix used in the particle filter in the same manner as in the Kalman filter example described above.

[0051] Next, a method for determining the filter strength when a low-pass filter is used in S403 will be described. The low-pass filter reduces the high-frequency components of the time-series data of the geographical location of the target 503. Determining the filter strength may include determining the cutoff frequency of the low-pass filter based on the distance L between the reference position 510 and the observation position 508. Lowering the cutoff frequency increases the filter strength, and raising the cutoff frequency decreases the filter strength.

[0052] In the embodiments described above, the method shown in Figure 4 is performed to obtain the geographical location of a dynamic target. Alternatively, or in addition to this, the method shown in Figure 4 may be performed to obtain the geographical location of a static or semi-static target. Observational data of static or semi-static targets may also contain observational noise. Therefore, by performing the method shown in Figure 4, the geographical location of a static or semi-static target can be determined with high accuracy.

[0053] In the above-described embodiment, the method shown in Figure 4 is performed by the control unit 201. Alternatively, at least one step of the method shown in Figure 4 may be performed by an external server connected to the mobile body 100 via a network. In other words, the method shown in Figure 4 may be performed by the cooperation of the external server and the control unit 201. In this case, the server and the control unit 201 constitute an information processing system for controlling the mobile body 100. Furthermore, when all steps of the method shown in Figure 4 are performed by the external server, the external server functions as an information processing device for performing the method shown in Figure 4.

[0054] <Summary of Embodiments> (Item 1) An information processing device (201) comprising: acquisition means for acquiring observation data (502) obtained by a sensor (206) that observes a target (503); determination means for determining the geographical location (508) of the target using the observation data; and filtering means for applying a filter to the time-series data of the geographical location to reduce the influence of observation noise, wherein the filtering means determines the strength of the filter based on the distance (L) between a reference position (510) related to the sensor and the geographical location of the target. According to this item, the geographical location of the target can be determined with high accuracy. (Item 2) The information processing device according to Item 1, wherein the filtering means determines the strength of the filter such that the strength of the filter increases as the distance between the reference position and the geographical location of the target increases. According to this item, the strength of the filter can be determined according to the characteristics of the influence of observation noise on the determination of the geographical location of the target. (Item 3) An information processing device according to Item 1 or 2, wherein determining the strength of the filter includes determining the magnitude of the observation noise based on the distance between a reference position associated with the sensor and the geographical position of the target. According to this item, the strength of the filter can be appropriately determined. (Item 4) An information processing device according to Item 3, wherein determining the magnitude of the observation noise includes determining the diagonal components of a covariance matrix representing the magnitude of the observation noise based on the distance between the reference position and the geographical position of the target. According to this item, the strength of the filter can be appropriately determined. (Item 5) An information processing device according to Item 4, wherein the filter is a Kalman filter. According to this item, the geographical position of the target can be determined with high accuracy. (Item 6) An information processing device according to Item 4, wherein the filter is a particle filter. According to this item, the geographical position of the target can be determined with high accuracy. (Item 7) An information processing device according to any one of Items 1 to 3, wherein the filter is a low-pass filter that reduces the high-frequency components of the time-series data of the geographical position. According to this item, the geographical position of the target can be determined with high accuracy.(Item 8) The information processing device according to Item 7, wherein determining the strength of the filter includes determining the cutoff frequency of the low-pass filter based on the distance between the reference position and the geographical position of the target. According to this item, the strength of the filter can be appropriately determined. (Item 9) The information processing device according to any one of Items 1 to 8, wherein the sensor includes a camera (501) and the observation data includes an image (502) captured by the camera. According to this item, the geographical position of the target can be accurately determined in camera observations. (Item 10) The information processing device according to Item 9, wherein determining the strength of the filter includes determining the diagonal components of a covariance matrix representing the magnitude of the observation noise such that the diagonal components increase on the order of the second order of the distance between the reference position and the geographical position of the target. According to this item, the geographical position of the target can be accurately determined in camera observations. (Item 11) The information processing device according to any one of Items 1 to 8, wherein the sensor includes a lidar and the observation data includes the distance observed by the lidar. According to this item, the geographical location of a target can be determined with high accuracy in observation by a LiDAR. (Item 12) The information processing device according to any one of items 1 to 11, further comprising control means for controlling the movement of a mobile body (100) equipped with the sensor based on the geographical location after the filter has been applied. According to this item, the mobile body can be moved appropriately. (Item 13) A mobile body (100) comprising the information processing device (201) according to any one of items 1 to 12, and a sensor (206) for observing a target (503). According to this item, a mobile body capable of accurately determining the geographical location of a target is provided. (Item 14) A program for causing a computer to function as the information processing device according to any one of items 1 to 12. According to this item, a program is provided for realizing a mobile body capable of accurately determining the geographical location of a target.(Item 15) An information processing method comprising: acquiring observation data (502) obtained by a sensor (206) that observes a target (503) (S401); determining the geographical location (508) of the target using the observation data (S402); and applying a filter to the time-series data of the geographical location to reduce the influence of observation noise (S403), wherein the strength of the filter is determined based on the distance (L) between a reference position (510) related to the sensor and the geographical location of the target. According to this item, the geographical location of the target can be determined with high accuracy. (Item 16) A system comprising: acquisition means for acquiring observation data (502) obtained by a sensor (206) that observes a target (503); determination means for determining the geographical location (508) of the target using the observation data; and filtering means for applying a filter to the time-series data of the geographical location to reduce the influence of observation noise, wherein the filtering means determines the strength of the filter based on the distance (L) between a reference position (510) associated with the sensor and the geographical location of the target. According to this item, the geographical location of a target can be determined with high accuracy.

[0055] The invention is not limited to the embodiments described above, and various modifications and changes are possible within the scope of the gist of the invention.

[0056] 100 Mobile unit, 201 Control unit, 501 Camera, 503 Target, 505 Actual position, 508 Observation position

Claims

1. An information processing device comprising: acquisition means for acquiring observation data obtained by a sensor that observes a target; determination means for determining the geographical location of the target using the observation data; and filtering means for applying a filter to time-series data of the geographical location to reduce the influence of observation noise, wherein the filtering means determines the strength of the filter based on the distance between a reference position related to the sensor and the geographical location of the target.

2. The information processing apparatus according to claim 1, wherein the filtering means determines the strength of the filter such that the strength of the filter increases as the distance between the reference position and the geographical position of the target increases.

3. The information processing apparatus according to claim 1 or 2, wherein determining the intensity of the filter includes determining the magnitude of the observation noise based on the distance between a reference position associated with the sensor and the geographical position of the target.

4. The information processing apparatus according to claim 3, wherein determining the magnitude of the observation noise includes determining the diagonal components of a covariance matrix representing the magnitude of the observation noise based on the distance between the reference position and the geographical position of the target.

5. The information processing apparatus according to claim 4, wherein the filter is a Kalman filter.

6. The information processing apparatus according to claim 4, wherein the filter is a particle filter.

7. The information processing apparatus according to any one of claims 1 to 3, wherein the filter is a low-pass filter that reduces the high-frequency components of the time-series data of the geographical location.

8. The information processing apparatus according to claim 7, wherein determining the intensity of the filter includes determining the cutoff frequency of the low-pass filter based on the distance between the reference position and the geographical position of the target.

9. The information processing apparatus according to any one of claims 1 to 8, wherein the sensor includes a camera, and the observation data includes an image captured by the camera.

10. The information processing apparatus according to claim 9, wherein determining the intensity of the filter includes determining the diagonal components of a covariance matrix representing the magnitude of the observation noise such that the diagonal components increase on the order of the second order of the distance between the reference position and the geographical position of the target.

11. The information processing apparatus according to any one of claims 1 to 8, wherein the sensor includes a lidar, and the observation data includes the distance observed by the lidar.

12. The information processing apparatus according to any one of claims 1 to 11, further comprising control means for controlling the movement of a mobile body equipped with the sensor based on the geographical location after the filter has been applied.

13. A mobile body comprising an information processing device according to any one of claims 1 to 12, and a sensor for observing a target.

14. A program for causing a computer to function as an information processing device according to any one of claims 1 to 12.

15. An information processing method comprising: acquiring observation data obtained by a sensor observing a target; determining the geographical location of the target using the observation data; and applying a filter to time-series data of the geographical location to reduce the influence of observation noise, wherein the strength of the filter is determined based on the distance between a reference position related to the sensor and the geographical location of the target.

16. A system comprising: acquisition means for acquiring observation data obtained by a sensor observing a target; determination means for determining the geographical location of the target using the observation data; and filtering means for applying a filter to time-series data of the geographical location to reduce the influence of observation noise, wherein the filtering means determines the strength of the filter based on the distance between a reference position associated with the sensor and the geographical location of the target.

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