Information processing method, information processing device, and program

Abstract

An information processing method according to the present disclosure has a filter map generation process and a filtering process. In a filter map generation process, a position of a sensing space in which aliasing is assumed is defined in a filter map. In the filtering process, filter performance of a noise filter for filtering the sensing data is set for each position of the sensing space based on the filter map.

Description

Technical Field

[0001] This invention relates to information processing methods, information processing apparatus, and programs. Background Technology

[0002] To achieve autonomous driving, it is necessary to estimate one's own position and identify the surrounding environment. As a sensor used for identifying the surrounding environment, distance measurement sensors using the Laser Imaging Detection and Ranging (LiDAR) method are known.

[0003] Citation List

[0004] Patent documents

[0005] PTL1: WO 2018 / 020656 Summary of the Invention

[0006] Technical issues

[0007] Distance measurement sensors using LiDAR methods may misidentify distant objects as nearby objects due to aliasing. To prevent false detections, methods such as using noise filters to remove signals with low signal strength or performing smoothing in space or time to remove salt-and-pepper noise can be considered. However, uniform processing may even lead to the loss of necessary information.

[0008] In view of this, the present disclosure proposes an information processing method, information processing apparatus and program that can reduce erroneous detection caused by aliasing.

[0009] Solution to the problem

[0010] According to this disclosure, a computer-executed information processing method is provided. This information processing method includes: defining locations in a filter map where aliasing is assumed to occur in a sensing space, and setting filter performance for a noise filter performing filtering of sensing data for each location in the sensing space based on the filter map. According to this disclosure, an information processing apparatus and program are provided to enable a computer to implement the information processing method. Attached Figure Description

[0011] Figure 1 This is an example diagram depicting a color image and a depth image of the space near the window.

[0012] Figure 2 It is a diagram depicting the effect of reducing aliasing through a noise filter.

[0013] Figure 3 It is a diagram depicting the effect of reducing aliasing through a noise filter.

[0014] Figure 4 This is a diagram depicting examples of objects that are prone to aliasing.

[0015] Figure 5 It is a diagram depicting how to set the filter performance for regions where aliasing is assumed (hereinafter referred to as aliasing assumption regions).

[0016] Figure 6 This is a diagram illustrating an example of robot configuration.

[0017] Figure 7 This is a diagram illustrating an example of robot configuration.

[0018] Figure 8 This is a diagram illustrating an example of the configuration of an information processing device for controlling a robot.

[0019] Figure 9 This is a diagram illustrating an example of filter information.

[0020] Figure 10 This is a diagram depicting an example of a filter graph.

[0021] Figure 11 This is a diagram illustrating an example of the processing flow.

[0022] Figure 12 This is a diagram illustrating an example of the hardware configuration of an information processing device. Detailed Implementation

[0023] Embodiments of this disclosure will now be described in detail with reference to the accompanying drawings. In each of the following embodiments, the same parts are indicated by the same reference numerals, and therefore repeated descriptions will be omitted.

[0024] Note that the descriptions will be given in the following order.

[0025] 1. Overview

[0026] 1-1. Aliasing in LiDAR Measurements

[0027] 1-2. Filter performance settings for the aliasing assumption region

[0028] 2. Robot Configuration

[0029] 3. Configuration of information processing devices

[0030] 4. Filter Information

[0031] 5. Filter Diagram

[0032] 6. Processing Flow

[0033] 7. Hardware Configuration Example

[0034] 8. Effects

[0035] 1. Overview

[0036] The following will refer to Figures 1 to 5 An overview of this disclosure is provided. The following description will focus on examples of autonomous driving performed indoors (e.g., in facilities such as factories); however, this disclosure is also applicable to autonomous driving in outdoor spaces.

[0037] 1-1. Aliasing in LiDAR Measurements

[0038] Figure 1 This diagram illustrates an example of acquiring color and depth images of the space near a window. A color image refers to an image obtained by detecting and imaging visible light using an image sensor. A depth image refers to an image obtained by performing grayscale display on the depth detected based on sensing data and imaging that depth.

[0039] When Automated Guided Vehicles (AGVs) are moving within a facility, they may mistakenly identify distant objects as nearby obstacles and then stop.

[0040] exist Figure 1 In the example, light entering from the window is reflected by the wall and illuminates the room. A clock is mounted on the wall, and light is strongly reflected from the clock's surface. The area enclosed by the dashed line is the object region OA, which indicates objects such as walls and clocks with high reflectivity. In the depth image, aliasing occurs in the light signals from the walls and clocks, which are the reflected light sources. The actual walls and clocks are located at a distance of 5.45m; however, due to aliasing, in an image with a distance measurement range of 5.0m, the walls and clocks are detected as obstacles at a distance of 0.45m.

[0041] Such false detections can be mitigated to some extent by using noise filters to remove signals with low signal strength, removing salt-and-pepper noise, etc.; however, depending on the object's reflectivity, size, and reflection angle, it may not be sufficient to completely eliminate false detections. Forcibly improving filter performance may even lead to the loss of necessary information.

[0042] Figure 2 and Figure 3 Each figure depicts the effect of reducing aliasing through a noise filter.

[0043] Figure 2 The left image is the depth image without a noise filter applied. Figure 2 The right image shows the depth image with the noise filter applied. Applying the noise filter improves error detection on the walls and clock sections, but it cannot sufficiently eliminate noise in areas with high reflectivity.

[0044] Figure 3 The left figure depicts the filtering results when the filter performance is low. Figure 3 The right-hand figure depicts the filtering results when the filter performance is high. When the filter performance is low, noise in the high-reflectivity portion cannot be adequately removed. Increasing the filter performance removes noise in the high-reflectivity portion, but may result in over-filtering or even loss of normal data.

[0045] aliasing issues can occur in various objects. Figure 4 This diagram illustrates examples of objects prone to aliasing. Examples of objects prone to aliasing include high-brightness light sources and highly reflective reflective components. When such components are present, conventional noise filters are insufficient to remove noise and may malfunction.

[0046] As mentioned above, when improving filter performance, adverse effects arise due to over-filtering. Furthermore, the inventors of this invention have discovered that false detections repeatedly occur in specific locations or objects. Therefore, in this disclosure, locations or objects prone to aliasing are identified during preliminary inspection. These identified locations or objects are designated as aliasing hypothesis regions AL (refer to...). Figure 5 ) is registered in the filter diagram MP (reference) Figure 8 In this context, selectively increasing the filter strength in the aliasing hypothesis region AL can reduce false detections while avoiding over-filtering.

[0047] 1-2. Filter performance settings for the aliasing assumption region

[0048] Figure 5 This is a diagram depicting how to set the filter performance for the aliasing assumption region AL.

[0049] The robot MB travels through an indoor passageway. An object that easily induces aliasing (aliasing-inducing object) is present ahead of the passageway. Examples of aliasing-inducing objects include white walls that induce aliasing through strong light reflection. The area containing aliasing-inducing objects is registered as the aliasing hypothesis region AL. A high-performance filter is applied to signals from the aliasing hypothesis region AL. A standard noise filter is applied to signals from objects other than aliasing-inducing objects.

[0050] Filter performance can be defined based on the type (algorithm) and strength (filter constants) of the noise filter. Different filter performances are assigned to each location within the space to be sensed (sensing space) based on the probability of aliasing. The filter graph FM defines the three-dimensional distribution of the filter performance. Based on the filter graph FM, the filter performance of the noise filter is set for each location in the sensing space. Setting the filter performance means, for example, setting at least one of the noise filter algorithm and filter constants.

[0051] For example, in Figure 1 In the example, instead of uniformly setting the filter performance for the entire data in the sensor's field of view, the filter performance is selectively increased for the data in the object region OA (e.g., a wall and clock with high reflectivity) corresponding to the aliasing hypothesis region AL. This prevents the loss of accurate information included in regions other than the object region OA. The specific configuration of the robot MB will be described below.

[0052] 2. Robot Configuration

[0053] Figure 6 and Figure 7 Each of these is a diagram illustrating an example configuration of the robot MB.

[0054] The robot MB is an autonomous mobile body that moves while recognizing its surroundings using technologies such as Simultaneous Localization and Mapping (SLAM). The robot MB includes a Time-of-Flight (ToF) camera (TF) and a 2D-LiDAR sensor (LD) as a distance measurement sensor (DS). One ToF camera (TF) and one 2D-LiDAR sensor (LD) are mounted in each of the front and rear sections of the robot MB. The robot MB includes a touch panel (DP) as a human-machine interface.

[0055] The Time-of-Flight (ToF) camera (TF) acquires 3D information by measuring the time-of-flight of each pixel arranged in a 2D array. The 2D-LiDAR sensor (LD) acquires 2D information by scanning a laser beam horizontally. The ToF camera (TF) is mounted at a height H1 above the ground, for example, 600 mm. The 2D-LiDAR sensor (LD) is mounted at a height H2, for example, 200 mm. The ToF camera (TF) has a measurement range θ along the elevation angle, for example, 50°.

[0056] Figure 6 and Figure 7 Each description depicts a type of robot MB that moves on wheels. Examples of this type of robot MB include delivery robots, catering robots, patrol robots, forklifts, wheel loaders, etc. However, the robot MB of this disclosure is not limited to this. The technology of this disclosure is also applicable to robot MBs of the type such as drones that fly using propellers.

[0057] 3. Configuration of information processing devices

[0058] Figure 8 This is a diagram illustrating an example configuration of the information processing unit PR that controls the robot MB.

[0059] The robot MB includes a distance measurement sensor DS, an angle sensor AS, a camera device, and an information database DB. C Filter information database DB F And information processing device PR. Camera device information database DB C Information related to the viewing angle of the ToF camera (TF) and the 2D-LiDAR sensor (LD) is stored as the viewing angle information (FV). Filter information database (DB) F Information related to the type and intensity of the noise filter is stored as filter information FI.

[0060] The distance measurement sensor DS measures the distance to the object. Based on the distance information, the distance measurement sensor DS generates a point cloud and a depth image. A point cloud refers to the point cloud obtained by projecting the depth image onto a three-dimensional coordinate system. The distance measurement sensor DS outputs the generated point cloud and depth image as sensing data RD. The angle sensor AS detects the tilt angle of the robot MB's reference coordinate system. The angle sensor AS outputs the detected tilt angle as angle information AD.

[0061] The information processing device PR is based on information from the distance measurement sensor DS, the angle sensor AS, and the camera device database DB. C and filter information database DB F The acquired data and information are used to estimate the robot MB's own position (LP) and plan its movement path. The information processing unit (PR) includes, for example, a filter graph (FM), a filter processing unit (FP), a self-position estimation unit (SL), an environment map building unit (MC), and a path planning unit (RP).

[0062] The Filter Map (FM) defines locations in the sensing space where aliasing is assumed. The sensing space refers to the operational space of the robot (MB) to be sensed by the distance measurement sensor (DS). The Filter Map (FM) includes information related to the distribution of filter performance in the sensing space (Performance Distribution Information, PF). For example, filter performance is defined by the type and strength of noise filters. Based on the filter information (FI), the Filter Map (FM) defines the type and strength of noise filters for each location in the sensing space.

[0063] The filter processing unit RP performs filtering on the sensed data RD using a noise filter. The filter processing unit FP sets the filter performance of the noise filter for each location in the sensed space based on the performance distribution information PF included in the filter map FM. The filter processing unit RP outputs the corrected data CD obtained by filtering the sensed data RD using the noise filter.

[0064] For example, the filter processing unit RP obtains information related to the robot MB's self-position LP and camera pose CP from the self-position estimation unit SL. Camera pose CP refers to the position and orientation of the ToF camera TF and the 2D-LiDAR sensor LD. The self-position estimation unit SL estimates the camera pose CP based on angle information AD. The filter processing unit RP retrieves information from the camera information database DB. C Acquire information related to the field of view (FV) of the ToF camera device (TF). The field of view (FV) includes the horizontal and vertical angles of view.

[0065] The filter processing unit RP estimates the sensing position of the distance measurement sensor DS (ToF sensor TF and 2D-LiDAR sensor LD) based on its own position LP and the camera device posture CP. The filter processing unit FP filters the sensing data RD by using a noise filter with filter performance associated with the sensing position.

[0066] The Environment Mapping Unit (MC) constructs an environment map (MP) of the sensed space based on calibration data (CD) and its own position (LP). The environment map MP is constructed, for example, as an occupancy grid map. The generated environment map MP is stored in a map database (not depicted). The Self-Position Estimation Unit (SL) performs estimation of its own position (LP) using the calibration data (CD) and the environment map MP. The self-position LP includes information related to the position and pose of the robot (MB). SLAM is used to perform the generation of the environment map MP and the estimation of the self-position LP.

[0067] The environment map (MP) is a high-precision map that reduces inaccuracies caused by aliasing. A filter map (FM) is generated by correlating the position on the environment map (MP) with filter performance (type and strength of noise filters). The path planning unit (RP) plans the movement path of the robot (MB) based on the environment map (MP) and its own position (LP). The RP can perform trajectory search to reach a specified destination along the movement path while avoiding obstacles. Known methods can be used for path planning and trajectory search.

[0068] 4. Filter Information

[0069] Figure 9 This is a diagram depicting an example of filter information FI.

[0070] The filter information FI includes information related to the filter algorithm (type of noise filter) and its strength (filter constants) used to remove noise included in the sensed data RD. Commonly used noise filters include brightness filters, dilation / erosion filters, time accumulation filters, spatial smoothing filters, etc. System developers can set the filter algorithm and filter constants for each aliasing hypothesis region AL.

[0071] The luminance filter removes luminance signals with luminance values ​​below a threshold. The strength of the luminance filter is represented by the luminance value used as the threshold (0 to 65535 in the 16-bit case). The dilation / erosion filter performs color replacement on the neighborhood of the target pixel (white or black) to have the same color as the target pixel. The strength of the dilation / erosion filter is represented by the range (number of pixels) of the neighboring pixels to which the color replacement is performed. The temporal accumulation filter accumulates data over time and smooths the data. The strength of the temporal accumulation filter is represented by the number of times the data is accumulated. The spatial smoothing filter uses the average pixel value of the target pixel and its neighboring pixels as the processed pixel value for the target pixel. The strength of the spatial smoothing filter is represented by the range of neighboring pixels. The range of neighboring pixels is represented as a range with m rows and n columns centered on the target pixel.

[0072] The noise filter described above is an example. Noise filters other than those described above are also applicable. For example, the filter described in JP 2021-50988 can be used as another noise filter.

[0073] 5. Filter Diagram

[0074] Figure 10 This is a diagram depicting an example of a filter graph FM.

[0075] The filter map FM indicates the distribution of filter performance in the environment map MP. Figure 10 "Region 1" and "Region 2" respectively indicate the aliasing hypothesis region AL. Figure 10 In the example, the filter performance of the noise filter is defined for each aliasing assumption region AL.

[0076] The aliasing hypothesis region AL is set as the location where aliasing is likely to occur. System developers can pre-designate the locations where strong noise filters will be applied (e.g., traffic signs (high reflectivity objects)) as aliasing hypothesis regions AL. Based on the likelihood of aliasing, system developers can differentiate the filter performance of the noise filters applied to each aliasing hypothesis region AL.

[0077] For example, in "Region 1", "Filter A" (brightness filter) and "Filter B" (expansion / erosion filter) are applied. The filter constant of "Filter A" is 10, and the filter constant of "Filter B" is also 10.

[0078] In "Region 2", "Filter A", "Filter B", "Filter C" (time accumulation filter) and "Filter D" (spatial smoothing filter) are applied. The filter constant of "Filter A" is 100, the filter constant of "Filter B" is 500, the filter constant of "Filter C" is 1, and the filter constant of "Filter D" is 3×3.

[0079] The aliasing hypothesis region AL can be specified using any method. For example, system developers can specify the aliasing hypothesis region AL by specifying the three-dimensional coordinates of the outer edge of the aliasing hypothesis region AL or by specifying the two-dimensional coordinates of the outer edge of the aliasing hypothesis region AL on a 2D map (such as a floor map).

[0080] System developers can also specify aliasing hypothesis regions (AL) based on the characteristics of objects in which aliasing may occur. For example, system developers can register the characteristics of objects in which aliasing may occur (e.g., the characteristics of the reflective portion of a circular velocity marker) in association with filter performance to the filter map (FM). The filter processing unit (FP) selectively improves the filter performance for sensing data (RD) from objects with the registered characteristics.

[0081] For example, the filter processing unit FP detects the hypothetical aliasing object region OA from the image of the captured sensing space based on the features of the object registered to the filter map FM. The image can be a color image captured by a visible light camera or a brightness image detected by a ToF camera TF. A color image is obtained by detecting and imaging visible light using an image sensor, and a brightness image is obtained by detecting and imaging infrared light using an image sensor.

[0082] The filter processing unit RP compares the image and the sensed data RD, and extracts the data region corresponding to the detected object region OA from the sensed data RD. The filter processing unit RP applies a noise filter with filter performance that enables the elimination of aliasing to the extracted data region.

[0083] 6. Processing Flow

[0084] Figure 11 This is a diagram illustrating an example of the processing flow.

[0085] The information processing device PR acquires sensing data RD from the distance measurement sensor DS (step S1). The information processing device PR retrieves information from the filter information database DB.F Obtain the default filter algorithm and filter constants (step S2).

[0086] The information processing device PR obtains the environmental map MP from the map database (step S3). The information processing device PR defines the distribution of filter performance in the environmental map MP based on the location information of the aliasing hypothesis region AL. The information processing device PR then obtains the map information relating the environmental map MP and the filter performance as the filter map FM (step S4).

[0087] The information processing device PR obtains its own position LP based on the sensing data RD (step S5). The information processing device PR retrieves information from the camera device's information database DB. C The viewing angle FV of the distance measurement sensor DS is acquired. The information processing unit PR acquires the camera device posture CP based on the angle information AD detected by the angle sensor AS (step S6).

[0088] The information processing unit PR estimates the current sensing position of the distance measurement sensor DS based on its own position LP, the camera device pose CP, and the viewing angle FV. The information processing unit PR then determines whether the sensing position is within the aliasing hypothesis region AL (step S7).

[0089] When the sensing location is within the aliasing hypothesis region AL (Yes in step S7), the information processing unit PR switches the filter performance of the noise filter to the filter performance associated with the aliasing hypothesis region AL based on the filter map FM (step S8). The information processing unit PR filters the sensing data RD based on the switched filter performance and generates a point cloud (step S9).

[0090] When the sensing location is not in the aliasing hypothesis region AL (No in step S7), the information processing device PR filters the sensing data RD based on the current filter performance and generates a point cloud (step S9).

[0091] Note that the order of the processes for reading information from the database (steps S2, S3, and S6) is not limited to... Figure 11 The order in which they appear.

[0092] 7. Hardware Configuration Example

[0093] Figure 12 This is a diagram illustrating an example of the hardware configuration of an information processing device PR;

[0094] Information processing in the information processing device PR is performed, for example, by a computer 1000. The computer 1000 includes a central processing unit (CPU) 1100, random access memory (RAM) 1200, read-only memory (ROM) 1300, hard disk drive (HDD) 1400, a communication interface 1500, and an input / output interface 1600. Each unit of the computer 1000 is connected via a bus 1050.

[0095] The CPU 1100 operates based on the program (program data 1450) stored in the ROM 1300 or HDD 1400 and controls each unit. For example, the CPU 1100 loads the program stored in the ROM 1300 or HDD 1400 into the RAM 1200 and executes the processing corresponding to the various programs.

[0096] ROM 1300 stores the startup program executed by CPU 1100 when computer 1000 starts, such as the Basic Input / Output System (BIOS), programs that depend on the hardware of computer 1000, etc.

[0097] HDD 1400 is a non-transitory computer-readable recording medium that non-transitory records programs to be executed by CPU 1100, data to be used by such programs, etc. Specifically, HDD 1400 is a recording medium that records an information processing program according to an embodiment (as an example of program data 1450).

[0098] Communication interface 1500 is an interface for connecting computer 1000 to an external network 1550 (e.g., the Internet). For example, CPU 1100 receives data from another device or sends data generated by CPU 1100 to another device via communication interface 1500.

[0099] Input / output interface 1600 is an interface for connecting input / output device 1650 and computer 1000. For example, CPU 1100 receives data from input devices such as keyboard and mouse via input / output interface 1600. CPU 1100 sends data to output devices such as display device, speaker and printer via input / output interface 1600. Input / output interface 1600 can be used as a media interface for reading programs recorded on a predetermined recording medium (medium). For example, the medium is an optical recording medium such as digital multifunction disc (DVD) and phase change rewritable disc (PD), a magneto-optical recording medium such as magneto-optical disc (MO), magnetic tape, magnetic recording medium, semiconductor memory, etc.

[0100] For example, when computer 1000 is used as an information processing device PR according to the embodiment, CPU 1100 of computer 1000 executes an information processing program loaded into RAM 1200, thereby implementing the functions of each of the above-described units. HDD 1400 stores information processing programs, various models, and various data according to this disclosure. Note that CPU 1100 reads program data 1450 from HDD 1400 and executes the program data 1450, but as another example, these programs may be obtained from another device via external network 1550.

[0101] 8. Effects

[0102] The information processing method disclosed herein includes a filter map FM generation process and a filtering process. In the filter map FM generation process, locations in the filter map FM where aliasing is assumed to occur in the sensing space are defined. In the filtering process, based on the filter map FM, the filter performance of a noise filter that performs filtering on the sensing data RD is set for each location in the sensing space. The information processing apparatus and program of this disclosure enable a computer 1000 to implement the information processing method of this disclosure.

[0103] Based on this configuration, regions where aliasing may occur are pre-registered in the filter graph FM. The filter performance can be adjusted according to the likelihood of aliasing, and thus, false detections caused by aliasing can be appropriately reduced.

[0104] The filter diagram FM defines the type and strength of the noise filter for each location in the sensing space.

[0105] Based on this configuration, the filter performance can be finely set according to the assumed state of the noise.

[0106] The information processing method disclosed herein includes the construction process of an environmental map MP. In the construction process of the environmental map MP, an environmental map MP of the sensed space is constructed based on correction data CD obtained by filtering the sensed data RD using a noise filter.

[0107] Based on this configuration, an accurate environment map MP is constructed.

[0108] The information processing method disclosed herein includes self-position estimation processing. In the self-position estimation processing, self-position LP is estimated by using calibration data CD and environmental map MP.

[0109] Based on this configuration, the user's position (LP) can be accurately estimated.

[0110] In the filtering process, the sensed position is estimated based on its own position LP. The sensed data RD is then filtered using a noise filter with filter performance associated with the sensed position.

[0111] Based on this configuration, the sensing position is accurately identified using information from the high-precision self-position LP. Therefore, the filter performance to be applied to the sensing position is appropriately extracted.

[0112] In the filtering process, an object region OA, where aliasing is assumed to occur, is detected from the image capturing the sensing space. In the filtering process, a noise filter with filter performance that allows aliasing to be eliminated is applied to the data region corresponding to the object region OA in the sensing data RD.

[0113] According to this configuration, the data of the object region OA that causes aliasing is appropriately filtered based on image information.

[0114] Note that the effects described in this specification are illustrative and not limiting, and other effects may be presented.

[0115] Supplementary Explanation

[0116] Note that this technology can also be configured as follows. (1)

[0118] An information processing method executed by a computer, the information processing method comprising:

[0119] Define the locations in the sensor space where aliasing is assumed in the filter diagram; and

[0120] Based on the filter diagram, the filter performance of the noise filter that performs filtering on the sensed data is set for each location in the sense space. (2)

[0122] According to the information processing method described in (1), wherein,

[0123] The filter diagram defines the type and strength of the noise filter for each location in the sensing space. (3)

[0125] The information processing method described in (1) or (2) above further includes:

[0126] An environmental map of the sensing space is constructed based on the correction data obtained by filtering the sensing data using the noise filter. (4)

[0128] The information processing method described in (3) above further includes:

[0129] The self-position is estimated by using the correction data and the environmental map. (5)

[0131] The information processing method described in (4) above further includes:

[0132] Estimate the sensed position based on its own position; and

[0133] The sensing data is filtered by using the noise filter, which has the filter performance associated with the sensing location. (6)

[0135] The information processing method according to any one of (1) to (5) above further includes:

[0136] Detect object regions in which the aliasing is assumed to occur from an image captured in the sensing space; and

[0137] The noise filter, which has the filter performance that enables the aliasing to be eliminated, is applied to the data region of the sensed data corresponding to the object region. (7)

[0139] An information processing apparatus, comprising:

[0140] Filter diagrams, which define the locations in the sensing space where aliasing is assumed; and

[0141] A filter processing unit is configured to set the filter performance of a noise filter that performs filtering of the sensed data for each location in the sense space based on the filter map. (8)

[0143] A program that enables a computer to perform the following functions:

[0144] Define the locations in the sensor space where aliasing is assumed in the filter diagram; and

[0145] Based on the filter diagram, the filter performance of the noise filter that performs filtering on the sensed data is set for each location in the sense space.

[0146] List of reference numerals

[0147] CD Correction Data

[0148] FM filter diagram

[0149] FP filter processing unit

[0150] LP's own position

[0151] MP Environment Map

[0152] OA object area

[0153] PR information processing device

[0154] RD sensing data

Claims

1. An information processing method executed by a computer, the information processing method comprising: Define the location of the sensing space where aliasing is assumed in the filter diagram; as well as Based on the filter diagram, the filter performance of the noise filter that performs filtering on the sensed data is set for each location in the sense space.

2. The information processing method according to claim 1, wherein, The filter diagram defines the type and strength of the noise filter for each location in the sensing space.

3. The information processing method according to claim 1 further includes: An environmental map of the sensing space is constructed based on the correction data obtained by filtering the sensing data using the noise filter.

4. The information processing method according to claim 3 further includes: The self-position is estimated by using the correction data and the environmental map.

5. The information processing method according to claim 4 further includes: The sensing position is estimated based on its own position; as well as The sensing data is filtered by using the noise filter, which has the filter performance associated with the sensing location.

6. The information processing method according to claim 1, further comprising: Detect object regions in which the aliasing is assumed to occur from an image captured in the sensing space; as well as The noise filter, which has the filter performance that enables the aliasing to be eliminated, is applied to the data region of the sensed data corresponding to the object region.

7. An information processing apparatus, comprising: A filter diagram, which defines the location of a sensing space where aliasing is assumed; as well as A filter processing unit is configured to set the filter performance of a noise filter that performs filtering of the sensed data for each location in the sense space based on the filter map.

8. A program that enables a computer to perform the following functions: Define the locations in the sensor space where aliasing is assumed in the filter diagram; and Based on the filter diagram, the filter performance of the noise filter that performs filtering on the sensed data is set for each location in the sense space.

Images