Indoor robot positioning method based on multi-description coding-decoding scheme

Abstract

The invention provides an indoor robot positioning method based on multi-description coding-decoding, and the technical key points are as follows: the method comprises the following steps: building a kinematic model of an indoor robot and a measurement model of a sensor, and building a mathematical model; constructing a state estimator of the indoor robot; and solving the upper bound of the covariance of the positioning error, and optimally designing parameters of an estimator to minimize the upper bound of the covariance of the positioning error, thereby realizing accurate positioning of the indoor robot. According to the method, the transmission opportunity of sensor data is actively adjusted through a token bucket communication protocol, burst flow is effectively inhibited, instantaneous overload of a channel is avoided, and efficient and smooth utilization of communication resources is realized; measurement information of each time is divided into two independent descriptions by adopting a multi-description coding-decoding scheme, and the two independent descriptions are transmitted in parallel through two independent communication links, so that even if partial descriptions are lost due to insufficient tokens or poor channels, a decoder can still perform effective reconstruction based on a single description, and the fault tolerance of an indoor robot positioning system is improved.

Description

Technical Field

[0001] This application relates to the technical field, and in particular to an indoor robot localization method based on a multi-description encoding-decoding scheme. Background Technology

[0002] In recent years, the rapid development of technologies such as edge computing, 5G communication, and artificial intelligence has provided strong technical support for the widespread application of indoor robots. For indoor robots to provide effective services in scenarios such as logistics, inspection, and home use, the most crucial element is their ability to possess high-precision and robust positioning capabilities. However, indoor robots often operate in complex environments with limited communication bandwidth. If sensors transmit data too frequently, it can easily cause communication network congestion, resulting in data loss or delays. This severely impacts the continuity and reliability of indoor robot positioning.

[0003] To conserve limited communication resources, industry often employs various communication methods to regulate the data transmission timing of sensors. Dynamic event triggering mechanisms, by setting trigger thresholds to determine whether measurement data can be transmitted, achieve this goal to some extent. However, the transmission timing under dynamic event triggering mechanisms is highly random and passive, easily leading to sudden, dense transmissions in situations of high external environmental uncertainty, resulting in instantaneous channel overload. This is especially true when using parallel transmission strategies such as multi-description encoding, where multiple descriptions may be sent simultaneously due to synchronous triggering, causing instantaneous channel overload. Once instantaneous channel overload occurs, all descriptions will be lost, impacting the performance of indoor robots. Summary of the Invention

[0004] The purpose of this application is to solve the technical problem that the transmission time of the dynamic event triggering mechanism in the prior art is prone to sudden dense transmission under the condition of high uncertainty of the external environment, thus causing instantaneous channel overload.

[0005] A method for indoor robot localization based on a multi-description encoding-decoding scheme under a token bucket communication protocol, characterized by comprising the following steps:

[0006] S1: Establish the kinematic model of the indoor robot and the measurement model of the sensors. The kinematic model is used to show the relationship between the state of the indoor robot and the control input, and the measurement model is used to show the relationship between the sensor's measurement of the distance and phase angle from the indoor robot to the landmark.

[0007] S2: Establish a mathematical model representing the token bucket communication protocol and the multi-description encoding-decoding scheme: The token bucket communication protocol controls the timing of sensor data transmission by dynamically updating the number of tokens in the bucket, allowing data transmission only when the number of tokens in the bucket meets the transmission threshold; The multi-description encoding-decoding scheme divides the measurement signal transmitted by the sensor into two independent description signals, which are transmitted in parallel to the decoder through two independent wireless links. The decoder reconstructs the original measurement signal based on the received description signals.

[0008] S3: Construct a state estimator for the indoor robot based on the measurement signal reconstructed by the decoder;

[0009] S4: Solve for the upper bound of the positioning error covariance, and minimize the upper bound of the positioning error covariance by optimizing the estimator parameters to achieve accurate positioning of the indoor robot.

[0010] Preferably, the method for setting the kinematic model of the robot in S1 is as follows:

[0011] set up Let this be the state vector of the indoor robot. For the control input of the indoor robot, the indoor robot system can be written as:

[0012]

[0013] in Indicates the location of the indoor robot. It is the azimuth angle; and For displacement velocity and angular velocity, For the sampling cycle of the indoor robot, The covariance is zero-mean Gaussian white noise. .

[0014] Preferably, the measurement model of the sensor in S1 is as follows:

[0015]

[0016] In the above formula, It is Gaussian white noise, and its covariance is expressed as ; This refers to the measurement performed by the sensor under ideal conditions, defined as follows:

[0017]

[0018] in From indoor robots to landmarks distance, This is the phase angle.

[0019] Preferably, the token communication protocol in S2 is as follows: Number of tokens in the bucket The update rules are given by the following formula:

[0020]

[0021] In the above formula, express The number of tokens in the bucket at any given time, and satisfy , Indicates in The number of tokens added to the bucket at any given time, and It represents the maximum capacity of the bucket; b indicates the maximum capacity of the bucket. The number of tokens required to transmit data at any given time. This is a variable indicating the transmission license.

[0022] Preferably, in step S2, a multi-description encoding-decoding scheme is used to process the measurement signal received by the encoder. Processing is performed on each measurement component. Two descriptive signals are generated using two independent encoding functions:

[0023] .

[0024] Preferably, the decoder's decoding rules are as follows:

[0025]

[0026] In the above formula, and It is an auxiliary decoder that reconstructs data using only a single description. It is a central decoder that achieves higher-precision reconstruction by combining two descriptions; if both descriptions are lost, the decoder retains the output from the previous moment.

[0027] Preferably, the estimator constructed in S3 is as follows:

[0028]

[0029] in For the parameters of the estimator to be designed, It is an innovative function, defined as follows:

[0030] .

[0031] Preferably, the upper bound matrix of the positioning error covariance in S4 and The following recurrence relation is satisfied:

[0032]

[0033] and

[0034]

[0035] The initial condition is: positive scalar and satisfy:

[0036]

[0037]

[0038] in ,but yes An upper bound, namely .

[0039] Preferably, the local estimator parameters in S4 Must meet

[0040]

[0041] in The covariance correlation matrix, For observation correlation matrix.

[0042] Preferably, the method further includes step 5: verifying the effectiveness of the proposed localization algorithm and conducting experimental verification.

[0043] The verification scheme includes the following steps:

[0044] Step 5-1: Construct a simulation experiment platform;

[0045] Step 5-2: Set basic parameters;

[0046] Step 5-3: 1) Calculate the estimator parameters according to the formula in S4. 2) Calculate the positioning information for the next step based on the formula in S3. Then, calculate the upper bound of the positioning error covariance according to the formula in S4. , and return to 1), until the end;

[0047] Step 5-4: Using the recursive least squares method, the final evaluation standard for the localization effect is shown in the following formula. ,in It is the mean square error.

[0048] Compared with the prior art, this application has the following beneficial effects:

[0049] 1) By introducing the token bucket communication protocol, the data transmission time of the indoor robot's sensors can be actively determined, effectively avoiding channel congestion caused by centralized transmission, and effectively reducing the consumption of communication resources while ensuring the continuity of positioning.

[0050] 2) A multi-description encoding-decoding scheme is adopted to divide a single measurement into multiple independent descriptions: even if some descriptions are missing due to the limited number of tokens or poor channel environment, the estimator can still achieve effective localization of the indoor robot based on the limited descriptions received.

[0051] 3) The token bucket communication protocol can provide a smooth and predictable transmission rhythm, which is naturally compatible with the multi-description coding's requirement for "asynchronous and distributed transmission". The two form a complementary architecture of "throttling + tolerance", which is particularly suitable for indoor wireless environments with limited bandwidth and complex interference. Attached Figure Description

[0052] Figure 1 This is a schematic diagram illustrating the steps of an indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol of the present invention.

[0053] Figure 2 This invention provides an indoor robot model for an indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol.

[0054] Figure 3 This is a schematic diagram of the framework of an indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol of the present invention;

[0055] Figure 4 This is a schematic diagram of the working mechanism of the token bucket communication protocol in an indoor robot localization method based on a multi-description encoding-decoding scheme according to the token bucket communication protocol of the present invention.

[0056] Figure 5 This is a schematic diagram illustrating the working mechanism of the multi-description encoding-decoding scheme in an indoor robot localization method based on the token bucket communication protocol according to the present invention.

[0057] Figure 6 This is a schematic diagram of the framework of an indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol of the present invention;

[0058] Figure 7 This is a diagram illustrating the estimation effect of the indoor robot's azimuth angle under a token bucket communication protocol based on a multi-description encoding-decoding scheme, as presented in this invention.

[0059] Figure 8The present invention relates to the logarithm of the minimum upper bound of the covariance of the positioning error and the logarithm of the mean square error of an indoor robot positioning method based on a multi-description encoding-decoding scheme under a token bucket communication protocol.

[0060] Figure 9 This invention relates to an indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol, specifically the data transmission timing of sensors under the token bucket communication protocol. Detailed Implementation

[0061] Please see Figure 1 This application provides an indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol, which includes the following steps:

[0062] S1: Establish the kinematic model of the indoor robot and the measurement model of the sensors;

[0063] Specifically, in the implementation method, it is set that Let this be the state vector of the indoor robot. For the control input of the indoor robot, the indoor robot system can be written as:

[0064]

[0065] in Indicates the location of the indoor robot. It is the azimuth angle. and These are displacement velocity and angular velocity. This refers to the sampling cycle of the indoor robot. The covariance is zero-mean Gaussian white noise. .

[0066] The sensor's measurement model for the robot's position is as follows:

[0067]

[0068] In the above formula, It is Gaussian white noise, and its covariance is expressed as . It is the measurement of the sensor under ideal conditions, defined as follows:

[0069]

[0070] in From indoor robots to landmarks distance, This is the phase angle.

[0071] S2: Establish a mathematical model that can characterize the token bucket communication protocol and the multi-description encoding-decoding scheme;

[0072] To save sensor communication resources and enable proactive adjustment of data transmission timing, this application introduces a novel token bucket communication protocol.

[0073] The token communication protocol is as follows:

[0074] exist At any given time, the number of valid tokens in the bucket is:

[0075]

[0076] in express The number of tokens in the time bucket Indicates in The number of tokens added to the bucket at any given time, and That is the maximum capacity of the bucket.

[0077] Transmission license indicator variable The definition is as follows:

[0078]

[0079] in Indicates in The number of tokens required to transmit data at any given time. The measured value is determined at Can time be transmitted: Only when The transmission request will only be accepted when the time is right.

[0080] Therefore, the number of tokens in the bucket The update rules are given by the following formula:

[0081]

[0082] in satisfy .

[0083] The measurement values ​​received by the encoder are shown below:

[0084]

[0085] To improve the utilization of channel resources and the reliability of transmission, a multi-description coding-decoding scheme is adopted to process the measurement signals received by the encoder. Processing is performed on each measurement component. Two descriptive signals are generated using two independent encoding functions.

[0086]

[0087] Subsequently, the signal was described. and The data is transmitted to the decoder via two independent wireless links. The success or failure of transmission on each link is determined by a Bernoulli random variable. and Modeling, satisfying:

[0088]

[0089] in It is a known constant, representing the link. The transmission success rate.

[0090] The decoder reconstructs the original measurement components based on the received description signals. Different decoding strategies are used. The decoding rules are as follows:

[0091]

[0092] in and It is an auxiliary decoder that reconstructs data using only a single description. It is a central decoder that achieves higher-precision reconstruction by combining two descriptions. If both descriptions are lost, the decoder retains the output from the previous moment.

[0093] S3: Build an estimator based on the received information;

[0094] In the implementation, an estimator is constructed based on the received decoded signal:

[0095]

[0096] in For the parameters of the estimator to be designed, It is an innovative function.

[0097] S4: Obtain the upper bound of the covariance of the positioning error and minimize it by reasonably designing the estimator parameters;

[0098] In the implementation, prediction error and prediction error covariance are defined as follows:

[0099] Prediction error:

[0100] Prediction error covariance: .

[0101] At the same time, define the positioning error and the positioning error covariance:

[0102] Positioning error:

[0103] Positioning error covariance: .

[0104] and It is a positive scalar. Given a positive scalar and given estimator parameters If the matrix and The following recurrence relation is satisfied:

[0105]

[0106] and

[0107]

[0108] Then in the initial conditions Down, yes An upper bound, namely .

[0109] Estimator parameters The upper bound can be made possible through the following design. Minimum:

[0110]

[0111] in

[0112]

[0113] S5: Verify the effectiveness of the proposed algorithm and conduct experimental verification.

[0114] Step 5-1: Construct a simulation experiment platform;

[0115] Step 5-2: Set basic parameters;

[0116] Step 5-3: The specific experimental steps are as follows: 1) Calculate the estimator parameters according to the formula in S4. 2) Calculate the positioning information for the next step based on the formula in S3. Then, calculate the upper bound of the positioning error covariance according to the formula in S4. , and return to 1), until the end;

[0117] Step 5-4: Using the recursive least squares method, the final evaluation standard for the localization effect is shown in the following formula. .in It is the mean square error.

[0118] The above content will be explained in conjunction with specific verification experiments:

[0119] An indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol includes the following steps:

[0120] Step 1: Establish the kinematic model of the indoor robot and the measurement model of the sensors.

[0121] In the implementation method, consider as follows Figure 2 The indoor robot shown is constructed using its kinematic model:

[0122] (1)

[0123] in Indicates the location of the indoor robot. It is the azimuth angle. and These are displacement velocity and angular velocity, respectively. and These are indoor robots shaft and The displacement velocity along the axial direction. For ease of computer operation, this application discretizes the above formula, resulting in the following formula:

[0124] (2)

[0125] in This refers to the sampling cycle of the indoor robot.

[0126] set up The status of the indoor robot.

[0127] For the control input of indoor robots,

[0128] Formula (2) can be written as:

[0129] (3)

[0130] in The covariance is zero-mean Gaussian white noise. .

[0131] Set up a landmark The coordinates are The distance from the indoor robot to the landmark is:

[0132] (4)

[0133] The phase angle is expressed as follows:

[0134] (5)

[0135] definition Furthermore, considering the influence of measurement noise, the following measurement model can be obtained:

[0136] (6)

[0137] in It is Gaussian white noise, and its covariance is expressed as... .

[0138] To handle system nonlinearity, At the estimated point Performing a Taylor series expansion at this point, we get:

[0139] (7)

[0140] in , and For a matrix of appropriate dimension, an uncertain matrix satisfy .

[0141] By applying Taylor series expansion once again, equation (6) can be expanded as follows:

[0142] (8)

[0143] in:

[0144]

[0145] In the above formula, and For a matrix of appropriate dimension, an uncertain matrix satisfy .

[0146] Step 2: Establish a mathematical model that can characterize the token bucket communication protocol and the multi-description encoding-decoding scheme.

[0147] In this implementation, to save sensor ground energy and actively adjust data transmission timing, a novel token bucket communication protocol is introduced, the working mechanism of which is as follows: Figure 4 As shown. In At any given time, the number of valid tokens in the bucket is:

[0148] (9)

[0149] in express The number of tokens in the time bucket Indicates in The number of tokens added to the bucket at any given time, and It is the maximum capacity of tokens in the bucket.

[0150] Transmission license indicator variable The definition is as follows:

[0151] (10)

[0152] in Indicates in The number of tokens required to transmit data at any given time. Decided to be in At any given moment, can the measured value be transmitted? Only when... The transmission request will only be accepted when the time is right.

[0153] Therefore, the number of tokens in the bucket The update rules are given by the following formula:

[0154] (11)

[0155] in satisfy .

[0156] The measurement values ​​received by the encoder are shown below:

[0157] (12)

[0158] To improve the utilization of channel resources and the reliability of transmission, a multi-description coding-decoding scheme is adopted to process the measurement signals received by the encoder. Process, such as Figure 5 As shown. For each measurement component Two descriptive signals are generated using two independent encoding functions.

[0159] (13)

[0160] Subsequently, the signal was described. and The data is transmitted to the decoder via two independent wireless links. The success or failure of transmission on each link is determined by a Bernoulli random variable. and Modeling, satisfying:

[0161] (14)

[0162] in It is a known constant, representing the link. The transmission success rate.

[0163] The decoder reconstructs the original measurement components based on the received description signals. Different decoding strategies are used. The decoding rules are as follows:

[0164] (15)

[0165] in and It is an auxiliary decoder that reconstructs data using only a single description. It is a central decoder that achieves higher-precision reconstruction by combining two descriptions. If both descriptions are lost, the decoder retains the output from the previous moment.

[0166] To facilitate subsequent analysis, the following lemma needs to be introduced first:

[0167] Each measurement element Each has a corresponding decoding error. It satisfies:

[0168] (16)

[0169] in , It is the saturation level. It is an adjustment factor. It is a positive scalar.

[0170] Define the following variables:

[0171]

[0172]

[0173]

[0174]

[0175] From the above definition, it is easy to know

[0176] (17)

[0177] Step 3: Build an estimator based on the received information.

[0178] In the implementation, to facilitate subsequent estimator design, the following innovative function is defined in this application:

[0179] (18)

[0180] By definition The innovation function can be further expressed as

[0181] （19）

[0182] Based on the received decoded signal, construct the estimator:

[0183] (20)

[0184] in For location information, These are the parameters of the estimator to be designed.

[0185] Step 4: Find the upper bound of the positioning error covariance and minimize it by reasonably designing the estimator parameters.

[0186] In the implementation method, the prediction error and the prediction error covariance are first defined as follows: and The positioning error and the positioning error covariance are defined as follows: and . and It is a positive scalar. Given a positive scalar, These are known estimator parameters. If the matrix... and The following recurrence relation is satisfied:

[0187] （21）

[0188] and

[0189] （22）

[0190] The initial condition is: positive scalar and satisfy:

[0191]

[0192]

[0193] in ,but yes An upper bound, namely .

[0194] Local estimator parameters The upper bound can be made possible through the following design. Minimum:

[0195] (twenty three)

[0196] in

[0197]

[0198] Step 5: Verify the effectiveness of the proposed localization algorithm and conduct experimental verification.

[0199] Specifically, the verification scheme includes the following steps:

[0200] Step 5-1: Construct a simulation experiment platform;

[0201] In the implementation, simulation experiments were conducted to verify the advantages and feasibility of the theoretical results. The indoor robot localization method based on multi-description encoding-decoding under the designed token bucket communication protocol was demonstrated in MATLAB (R2016a).

[0202] Step 5-2: Set basic parameters;

[0203] like Figure 3 As shown, the following system parameters are considered in the indoor robot system:

[0204] .

[0205] The number of steps in the experiment is Assume the sampling period of the indoor robot's odometry is 10.15 s, the angular velocity is 0.2 rad / s, and the displacement velocity is... m / s. Let the initial state of the indoor robot be... And the initial location information is Set up a landmark The position is Let the covariance of the robot system noise be... The covariance of the sensor measurement noise is For a multi-description encoding / decoding scheme, the expected value of the successful arrival rate of the encoder-to-decoder description is... The covariance of the success rate is 0.09. Set the saturation value to... Adjustable parameters are set to ,as well as Positive scalar Set as .parameter and Set as For the token bucket communication protocol, the token addition rate is set to [value missing] in this application. The number of tokens required for transmission is The maximum capacity of the bucket is And the initial number of tokens in the bucket is .

[0206] Step 5-3: The specific experimental steps are as follows: 1) Calculate the estimator parameters according to the formula in S4. 2) Calculate the positioning information for the next step. Upper bound of positioning error covariance , and return to 1), until the end;

[0207] Step 5-4: Using the recursive least squares method, the final evaluation standard for the positioning effect is determined by the mean square error, which is defined as: .

[0208] Please refer to the simulation results. Figures 6 to 9 . Figure 6 In the diagram, the blue solid line represents the actual movement trajectory of the indoor robot, and the red solid line represents the positioning trajectory. Figure 7 The image shows the positioning scheme's ability to estimate the azimuth angle. Figure 8 In the diagram, the solid blue line represents the logarithm of the trace of the minimum upper bound of the covariance of the indoor robot's positioning error, and the dashed red line represents the logarithm of the mean square error. Figure 9 This demonstrates the transmission timing of the sensor under the token bucket communication protocol.

[0209] This application provides an indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol. It addresses the localization problem of indoor robots using this method, innovatively combining the token bucket communication protocol with the multi-description encoding-decoding scheme. On one hand, the token bucket communication protocol actively adjusts the timing of sensor data transmission, effectively suppressing sudden traffic surges and avoiding instantaneous channel overload, achieving efficient and smooth utilization of communication resources. On the other hand, the multi-description encoding-decoding scheme divides each measurement information into two independent descriptions, which are transmitted in parallel via two independent communication links. Even if some descriptions are lost due to insufficient tokens or poor channel conditions, the decoder can still effectively reconstruct the system based on a single description, significantly improving the fault tolerance of the indoor robot localization system. This invention was supported by the National Natural Science Foundation of China (Grant No. 62403259).

Claims

1. An indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol, characterized in that: Includes the following steps: S1: Establish the kinematic model of the indoor robot and the measurement model of the sensors. The kinematic model is used to show the relationship between the state of the indoor robot and the control input, and the measurement model is used to show the relationship between the sensor's measurement of the distance and phase angle from the indoor robot to the landmark. S2: Establish a mathematical model representing the token bucket communication protocol and the multi-description encoding-decoding scheme: The token bucket communication protocol controls the timing of sensor data transmission by dynamically updating the number of tokens in the bucket, allowing data transmission only when the number of tokens in the bucket meets the transmission threshold; The multi-description encoding-decoding scheme divides the measurement signal transmitted by the sensor into two independent description signals, which are transmitted in parallel to the decoder through two independent wireless links. The decoder reconstructs the original measurement signal based on the received description signals. S3: Construct a state estimator for the indoor robot based on the measurement signal reconstructed by the decoder; S4: Solve for the upper bound of the positioning error covariance, and minimize the upper bound of the positioning error covariance by optimizing the estimator parameters to achieve accurate positioning of the indoor robot.

2. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: The method for setting the kinematic model of the robot in S1 is as follows: set up Let this be the state vector of the indoor robot. For the control input of the indoor robot, the indoor robot system can be written as: in Indicates the location of the indoor robot. It is the azimuth angle; and For displacement velocity and angular velocity, For the sampling cycle of the indoor robot, The covariance is zero-mean Gaussian white noise. .

3. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: The measurement model of the sensor in S1 is as follows: In the above formula, It is Gaussian white noise, and its covariance is expressed as ; This refers to the measurement performed by the sensor under ideal conditions, defined as follows: in From indoor robots to landmarks distance, This is the phase angle.

4. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: The token communication protocol in S2 is as follows: Number of tokens in the bucket The update rules are given by the following formula: In the above formula, express The number of tokens in the bucket at any given time, and satisfy , Indicates in The number of tokens added to the bucket at any given time, and That is the maximum capacity of the bucket; Indicates in The number of tokens required to transmit data at any given time. This is a variable indicating the transmission license.

5. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: In step S2, a multi-description encoding-decoding scheme is used to process the measurement signal received by the encoder. Processing is performed on each measurement component. Two descriptive signals are generated using two independent encoding functions: 。 6. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 5, characterized in that: The decoder's decoding rules are as follows: In the above formula, and It is an auxiliary decoder that reconstructs data using only a single description. It is a central decoder that achieves higher-precision reconstruction by combining two descriptions; if both descriptions are lost, the decoder retains the output from the previous moment.

7. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: The estimator constructed in S3 is as follows: in For the parameters of the estimator to be designed, It is an innovative function, defined as follows: 。 8. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: The upper bound matrix of the positioning error covariance in S4 and The following recurrence relation is satisfied: and The initial condition is: positive scalar and satisfy: If #imgpt62# is an upper bound of #imgpt64#, then #imgpt63# is an upper bound of #imgpt64#, namely #imgpt65#.

9. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: The local estimator parameter #imgpt66# in S4 must satisfy the following: #imgpt67# Where #imgpt68# is the covariance correlation matrix and #imgpt69# is the observation correlation matrix.

10. The indoor robot localization method based on a multi-description encoding-decoding scheme under the token bucket communication protocol according to claim 1, characterized in that: The algorithm also includes step 5: verifying the effectiveness of the proposed localization algorithm and conducting experimental verification. The verification scheme includes the following steps: Step 5-1: Construct a simulation experiment platform; Step 5-2: Set basic parameters; Step 5-3: 1) Calculate the estimator parameter #imgpt70# according to the formula in S4; 2) Calculate the positioning information #imgpt71# in the next step according to the formula in S3, then calculate the upper bound of the positioning error covariance #imgpt72# according to the formula in S4, and return to 1) until the end; Step 5-4: Using the recursive least squares method, the final evaluation standard for the positioning effect is shown in the following formula #imgpt73#, where #imgpt74# is the mean square error.

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