Multi-cycle time difference weighted ranging method based on obstacle perception

Abstract

The invention discloses a multi-period time difference weighted ranging method based on obstacle perception, which relates to the technical field of intelligent perception and sensor fusion, and specifically comprises the steps of multi-period time difference measurement, obstacle attenuation coefficient modeling, weighted fusion calculation, dynamic error compensation and final distance output. According to the multi-period time difference weighted distance measurement method based on obstacle perception, through multi-period time difference measurement and weighted fusion, random errors are reduced, and the distance measurement precision is effectively improved; by introducing the number of obstacles as a key variable, the weight of a distance measurement formula is dynamically adjusted to adapt to a complex scene, and dynamic environment adaptation is realized; and through combination with an obstacle attenuation model, the influence of multi-path interference on a distance measurement result is effectively suppressed, and the anti-interference capability is effectively enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent perception and sensor fusion, and in particular to a multi-cycle time difference weighted ranging method based on obstacle perception. Background Art

[0002] Time-of-flight (ToF) ranging technology is a method of calculating the distance to a target object by measuring the propagation time of light, sound waves, or other signals between a transmitter and a receiver. Existing ToF ranging technologies are typically based on a single time difference measurement, estimating distance by calculating the difference in the round-trip time of the signal.

[0003] However, this method has the following problems in practical application:

[0004] 1. There is a problem of multipath interference. In complex environments, the signal may be reflected multiple times, resulting in distortion of timestamp acquisition and reduced ranging accuracy.

[0005] 2. Traditional methods do not consider the dynamic changes of obstacles in the environment and therefore cannot adapt to complex scenarios;

[0006] 3. Existing technologies typically rely solely on signal strength or fixed temperature models for compensation, lacking the ability to perceive the number and material of obstacles. This results in large deviations in ranging results and insufficient error compensation.

[0007] To this end, this application proposes a multi-cycle time difference weighted ranging method based on obstacle perception to solve the above problems. Summary of the Invention

[0008] (1) Technical problems solved

[0009] In view of the shortcomings of the existing technology, the present invention provides a multi-cycle time difference weighted ranging method based on obstacle perception, which solves the technical problems raised in the above background.

[0010] (2) Technical solution

[0011] To achieve the above objectives, the present invention adopts a technical solution: a multi-cycle time difference weighted ranging method based on obstacle perception, which includes the following steps:

[0012] Step 1: Multi-cycle time difference measurement

[0013] In a single ranging cycle, multiple time difference measurements are performed continuously, and the timestamp data of each measurement is recorded. Each measurement includes the following time difference:

[0014] *Rai: The difference between the response receiving timestamp and the poll sending timestamp;

[0015] *Rbii: the difference between the final receiving timestamp and the response sending timestamp;

[0016] *Daii: the difference between the final sending timestamp and the response receiving timestamp;

[0017] *Dbii: The difference between the response sending timestamp and the polling receiving timestamp;

[0018] Step 2: Obstacle attenuation coefficient modeling

[0019] Detect the number of obstacles Nobstacle through the environment perception module;

[0020] Define the obstacle attenuation coefficient β:

[0021] β=e -K·Nobstocle ;

[0022] Among them, k is the material attenuation constant, which is dynamically adjusted according to the obstacle material;

[0023] Step 3: Weighted fusion calculation

[0024] Perform weighted fusion on the time difference data of each measurement to calculate the final time of flight ToF:

[0025]

[0026] Where n is the number of measurement cycles, βi is the obstacle attenuation coefficient of the i-th measurement, and γ(T) is the temperature drift compensation term;

[0027] Step 4: Dynamic Error Compensation

[0028] According to the number of obstacles and material type, the pre-stored compensation parameter table is called to dynamically adjust the distance measurement results;

[0029] The compensation parameter table contains correction values for typical scenarios;

[0030] Step 5: Output the final distance

[0031] Calculate the distance based on the final time of flight ToF:

[0032] Distance=ToF final ·SPEED_OF_LIGHT.

[0033] Preferably, in the step 1, the number of times the time difference measurements are continuously performed within a single ranging cycle may be 3 or 5 times.

[0034] Preferably, the environment perception module in step 2 may be a millimeter wave radar or a visual sensor.

[0035] Preferably, the material of the obstacle in step 2 is metal, concrete, or wood.

[0036] Preferably, the k values of different materials in step 2 are pre-calibrated and stored through experiments.

[0037] Preferably, the three temperature drift compensation items of γ(T) in the step are calculated in real time by a temperature sensor.

[0038] Preferably, the step five further includes performing a secondary correction on the distance value to eliminate system errors.

[0039] (3) Beneficial effects

[0040] The beneficial effects of the present invention are:

[0041] This multi-cycle time difference weighted ranging method based on obstacle perception reduces random errors and effectively improves ranging accuracy through multi-cycle time difference measurement and weighted fusion. By introducing the number of obstacles as a key variable, the weight of the ranging formula is dynamically adjusted to adapt to complex scenarios and achieve dynamic adaptation to the environment. By combining the obstacle attenuation model, the impact of multipath interference on ranging results is effectively suppressed, effectively enhancing anti-interference capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Schematic diagram of the system of the present invention. DETAILED DESCRIPTION

[0043] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, the present invention provides a technical solution: a multi-cycle time difference weighted ranging method based on obstacle perception, the method comprising the following steps:

[0045] Step 1: Multi-cycle time difference measurement

[0046] In a single ranging cycle, multiple time difference measurements are performed continuously, and the timestamp data of each measurement is recorded. Each measurement includes the following time differences. The number of times multiple time difference measurements are performed continuously can be 3 or 5:

[0047] *Rai: The difference between the response receiving timestamp and the poll sending timestamp;

[0048] *Rbii: the difference between the final receiving timestamp and the response sending timestamp;

[0049] *Daii: the difference between the final sending timestamp and the response receiving timestamp;

[0050] *Dbii: The difference between the response sending timestamp and the polling receiving timestamp;

[0051] Step 2: Obstacle attenuation coefficient modeling

[0052] Detect the number of obstacles through the environment perception module, which can be a millimeter wave radar or a visual sensor;

[0053] Define the obstacle attenuation coefficient β:

[0054] β=e -K·Nobstacle ;

[0055] Where k is the material attenuation constant, which is dynamically adjusted according to the obstacle material. The obstacle materials are specifically metal, concrete, and wood. The k values of different obstacle materials are pre-calibrated and stored through experiments.

[0056] Step 3: Weighted fusion calculation

[0057] Perform weighted fusion on the time difference data of each measurement to calculate the final time of flight ToF:

[0058]

[0059] Where n is the number of measurement cycles, βi is the obstacle attenuation coefficient of the i-th measurement, and γ(T) is the temperature drift compensation term. The temperature drift compensation term of γ(T) is calculated in real time by the temperature sensor.

[0060] Step 4: Dynamic Error Compensation

[0061] According to the number of obstacles and material type, the pre-stored compensation parameter table is called to dynamically adjust the distance measurement results;

[0062] The compensation parameter table contains correction values for typical scenarios;

[0063] Step 5: Output the final distance

[0064] Calculate the distance based on the final time of flight ToF:

[0065] Distance=ToF final SPEED_OF_LIGHT

[0066] Finally, the distance value is corrected twice to eliminate the systematic error.

[0067] Example

[0068] A multi-cycle time difference weighted ranging method based on obstacle perception, the method comprising the following steps:

[0069] Step 1: Multi-cycle time difference measurement

[0070] In a single ranging cycle, multiple time difference measurements are performed continuously, and the timestamp data of each measurement is recorded. Each measurement includes the following time differences. The number of times multiple time difference measurements are performed continuously can be 3 or 5:

[0071] *Rai: The difference between the response receiving timestamp and the poll sending timestamp;

[0072] *Rbii: the difference between the final receiving timestamp and the response sending timestamp;

[0073] *Daii: the difference between the final sending timestamp and the response receiving timestamp;

[0074] *Dbii: The difference between the response sending timestamp and the polling receiving timestamp;

[0075] Step 2: Obstacle attenuation coefficient modeling

[0076] Detect the number of obstacles through the environment perception module, which can be a millimeter wave radar or a visual sensor;

[0077] Define the obstacle attenuation coefficient β:

[0078] β=e -K·Nobstacle

[0079] Where k is the material attenuation constant, which is dynamically adjusted according to the obstacle material. The obstacle materials are specifically metal, concrete, and wood. The k values of different obstacle materials are pre-calibrated and stored through experiments.

[0080] Step 3: Weighted fusion calculation

[0081] Perform weighted fusion on the time difference data of each measurement to calculate the final time of flight ToF:

[0082]

[0083] Where n is the number of measurement cycles (e.g., 3 or 5), βi is the obstacle attenuation coefficient of the i-th measurement, and γ(T) is the temperature drift compensation term. The temperature drift compensation term of γ(T) is calculated in real time by the temperature sensor.

[0084] Step 4: Dynamic Error Compensation

[0085] According to the number of obstacles and material type, the pre-stored compensation parameter table is called to dynamically adjust the distance measurement results;

[0086] The compensation parameter table contains correction values for typical scenarios.

[0087] For example:

[0088] When there are no obstacles, the compensation coefficient is 1.0;

[0089] When there is one metal obstacle, the compensation coefficient is 0.872;

[0090] When there are two concrete obstacles, the compensation coefficient is 0.834;

[0091] Step 5: Output the final distance

[0092] Calculate the distance based on the final time of flight ToF:

[0093] Distance=ToF final SPEED_OF_LIGHT

[0094] Finally, the distance value is corrected twice to eliminate the systematic error.

[0095] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-cycle time difference weighted ranging method based on obstacle perception, characterized by: The method comprises the following steps: Step 1: Multi-cycle time difference measurement In a single ranging cycle, multiple time difference measurements are performed continuously, and the timestamp data of each measurement is recorded. Each measurement includes the following time difference: *Rai: The difference between the response receiving timestamp and the poll sending timestamp; *Rbii: the difference between the final receiving timestamp and the response sending timestamp; *Daii: the difference between the final sending timestamp and the response receiving timestamp; *Dbii: The difference between the response sending timestamp and the polling receiving timestamp; Step 2: Obstacle attenuation coefficient modeling Detect the number of obstacles Nobstacle through the environment perception module; Define the obstacle attenuation coefficient β: β＝e -K·Nobstacle ； Among them, k is the material attenuation constant, which is dynamically adjusted according to the obstacle material; Step 3: Weighted fusion calculation Perform weighted fusion on the time difference data of each measurement to calculate the final time of flight ToF: Where n is the number of measurement cycles, βi is the obstacle attenuation coefficient of the i-th measurement, and γ(T) is the temperature drift compensation term; Step 4: Dynamic Error Compensation According to the number of obstacles and material type, the pre-stored compensation parameter table is called to dynamically adjust the distance measurement results; The compensation parameter table contains correction values for typical scenarios; Step 5: Output the final distance Calculate the distance based on the final time of flight ToF: Distance=ToF final ·SPEED_OF_LIGHT。 2. The multi-cycle time difference weighted ranging method based on obstacle perception according to claim 1, characterized in that: In the step 1, the number of times the time difference measurements are continuously performed within a single ranging cycle may be 3 or 5.

3. The multi-cycle time difference weighted ranging method based on obstacle perception according to claim 1, characterized in that: The environment perception module in step 2 may be a millimeter wave radar or a visual sensor.

4. The multi-cycle time difference weighted ranging method based on obstacle perception according to claim 1, characterized in that: The obstacle material in step 2 is specifically metal, concrete, or wood.

5. The multi-cycle time difference weighted ranging method based on obstacle perception according to claim 4, characterized in that: The k values of different materials in step 2 are pre-calibrated and stored through experiments.

6. The multi-cycle time difference weighted ranging method based on obstacle perception according to claim 1, characterized in that: In the steps, the three temperature drift compensation items of γ(T) are calculated in real time by a temperature sensor.

7. The multi-cycle time difference weighted ranging method based on obstacle perception according to claim 1, characterized in that: The step five also includes performing a secondary correction on the distance value to eliminate system errors.

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