Pose determination method and device, equipment, storage medium and computer program product
By acquiring IMU data packets and predicting pose information at the current pose determination time, the problem of poor pose determination reliability in the prior art is solved, the output frequency and reliability of pose information are improved, and the needs of vehicle control are met.
Patent Information
- Application Number
- CN202311735040.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-15
- Publication Date
- 2025-06-24
AI Technical Summary
In the prior art, when determining vehicle position information based on IMU data, there is a problem that the position determination reliability is poor.
By acquiring the first IMU data packet of the vehicle at the current pose determination time, obtaining the current pose information, and predicting the predicted pose information of the vehicle at at least one prediction time based on the current pose information and the last frame target IMU data in the first IMU data packet before acquiring the second IMU data packet.
The output frequency of position information and the reliability of position determination are improved, and the requirements of vehicle control can be met.
Smart Images
Figure CN120198488A_ABST
Abstract
Description
Technical Field
[0001] Embodiments of the present disclosure relate to the technical field of vehicles, and in particular, to a method, device, equipment, storage medium, and computer program product for determining pose. Background Art
[0002] An Inertial Measurement Unit (IMU) is a device that measures the attitude angle (or angular rate) and acceleration of an object. Usually, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, which are respectively used to measure the acceleration data and angular velocity data of an object in three-dimensional space, that is, IMU data.
[0003] IMU data is widely used in projects such as autonomous driving, navigation systems, and driving safety. For example, IMU data can be used to calculate the pose information of a vehicle, thereby being used for vehicle positioning.
[0004] However, when currently determining the pose information of a vehicle based on IMU data, there is a problem of poor reliability in pose determination. Summary of the Invention
[0005] Embodiments of the present disclosure provide a method, device, equipment, storage medium, and computer program product for determining pose, which can improve the reliability of pose determination.
[0006] In a first aspect, embodiments of the present disclosure provide a method for determining pose. The method includes:
[0007] Obtaining a first IMU data packet of a vehicle at a current pose determination moment, where the first IMU data packet includes multiple frames of target IMU data collected at different moments;
[0008] Obtaining the current pose information of the vehicle at the current pose determination moment according to the first IMU data packet;
[0009] Before obtaining a second IMU data packet of the vehicle, predicting the predicted pose information of the vehicle at at least one prediction moment based on the current pose information and the last frame of target IMU data in the first IMU data packet, where the prediction moment is a moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
[0010] In a second aspect, embodiments of the present disclosure provide a device for determining pose. The device includes:
[0011] An obtaining module, configured to obtain a first IMU data packet of a vehicle at a current pose determination moment, where the first IMU data packet includes multiple frames of target IMU data collected at different moments;
[0012] A pose determination module, configured to obtain current pose information of the vehicle at the current pose determination moment according to the first IMU data packet;
[0013] A pose prediction module, configured to, before obtaining the second IMU data packet of the vehicle, predict predicted pose information of the vehicle at at least one prediction moment based on the current pose information and the last frame of target IMU data in the first IMU data packet, where the prediction moment is a moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
[0014] In a third aspect, an embodiment of the present disclosure provides an electronic device. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method described in the first aspect above is implemented.
[0015] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the method described in the first aspect above is implemented.
[0016] In a fifth aspect, an embodiment of the present disclosure provides a computer program product. The computer program product includes a computer program. When the computer program is executed by a processor, the method described in the first aspect above is implemented.
[0017] The pose determination method, device, equipment, storage medium, and computer program product provided by the embodiments of the present disclosure obtain the first IMU data packet of the vehicle at the current pose determination moment. The first IMU data packet includes multiple frames of target IMU data collected at different moments. Then, the current pose information of the vehicle at the current pose determination moment is obtained according to the first IMU data packet. Then, before obtaining the second IMU data packet of the vehicle, the predicted pose information of the vehicle at at least one prediction moment is predicted based on the current pose information and the last frame of target IMU data in the first IMU data packet. The prediction moment is a moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet. In this way, the embodiments of the present disclosure predict the predicted pose information at at least one prediction moment between the current pose determination moment and the next pose determination moment. Compared with the traditional technology of determining the pose information of the vehicle based on IMU data, where the pose information of the vehicle is only calculated at the moment when the IMU data packet is received, resulting in a low output frequency of the pose information and poor reliability of pose determination, the embodiments of the present disclosure add the determination of the predicted pose information between the current pose determination moment and the next pose determination moment, thereby improving the output frequency of the pose information and the reliability of pose determination, which is beneficial to meeting the requirements of vehicle control. Brief Description of the Drawings
[0018] Figure 1 It is an application environment diagram of the pose determination method in an embodiment;
[0019] Figure 2 It is a schematic flowchart of the pose determination method in an embodiment;
[0020] Figure 3 It is a timing diagram of an exemplary pose determination method in another embodiment;
[0021] Figure 4 It is a schematic flowchart of step 202 in another embodiment;
[0022] Figure 5 It is a schematic flowchart of the pose determination method in another embodiment;
[0023] Figure 6 It is a schematic flowchart of step 201 in another embodiment;
[0024] Figure 7 It is an experimental effect diagram taking the heading angle output as an example in another embodiment;
[0025] Figure 8 It is a schematic flowchart of the pose determination method in another embodiment;
[0026] Figure 9 It is a structural block diagram of the pose determination device in an embodiment;
[0027] Figure 10 It is an internal structure diagram of an electronic device in an embodiment. Detailed Embodiments
[0028] In order to make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the following further details the embodiments of the present disclosure in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present disclosure and are not used to limit the embodiments of the present disclosure.
[0029] First, before specifically introducing the technical solutions of the embodiments of the present disclosure, the technical background or the technical evolution context on which the embodiments of the present disclosure are based will be introduced.
[0030] An inertial measurement unit (IMU) is a device for measuring the attitude angle (or angular rate) and acceleration of an object. Usually, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, which are respectively used to measure the acceleration data and angular velocity data of an object in three-dimensional space, that is, IMU data.
[0031] IMU data is widely used in projects such as autonomous driving, navigation systems, and driving safety. For example, IMU is an essential sensor for the positioning module in an intelligent driving system. The core value of IMU lies in its ability to work independently, and the output frequency of IMU data is not less than 100 Hz. In a fusion positioning system based on Kalman filtering, IMU is usually used as the main sensor for inertial navigation recursive solution, and the observation updates of other sensors are added during the solution process.
[0032] Under normal circumstances, when the IMU acquires one frame of IMU data, it outputs one frame of IMU data, and when the processor processes one frame of IMU data, it can estimate the pose information once. The pose information usually includes the three-dimensional position and three-dimensional attitude of the vehicle.
[0033] However, some low-cost IMUs (such as the M8 and F9 series chips of u-blox) are restricted by communication bandwidth and output frequency. Although the acquisition frequency of the original IMU data is 100 Hz, the actual output frequency of IMU data is 10 Hz, that is, each packet of IMU data contains the previous 10 beats of IMU data.
[0034] This data output method not only causes inconvenience in algorithm processing but also destroys the core advantage of the high output frequency of IMU. As a result, the output frequency of pose information is reduced to 10 Hz, leading to a decrease in the reliability of pose determination, directly resulting in a reduction in the output frequency of the positioning result based on pose information to 10 Hz. However, a positioning output frequency of 10 Hz cannot meet the requirements of vehicle control.
[0035] In addition, it should be noted that the applicant has put in a lot of creative labor from discovering the above technical problems and the technical solutions introduced in the following embodiments.
[0036] Next, in combination with the scenarios applied in the embodiments of the present disclosure, the technical solutions involved in the embodiments of the present disclosure will be introduced.
[0037] The pose determination method provided by the embodiments of the present disclosure can be applied to the Figure 1 shown implementation environment. Among them, vehicle 110 obtains the first IMU data packet of vehicle 110 at the current pose determination moment. The first IMU data packet includes multiple frames of target IMU data collected at different times; vehicle 110 obtains the current pose information of vehicle 110 at the current pose determination moment according to the first IMU data packet; before vehicle 110 obtains the second IMU data packet of vehicle 110, based on the current pose information and the last frame of target IMU data in the first IMU data packet, vehicle 110 predicts the predicted pose information of vehicle 110 at at least one prediction moment. The prediction moment is the moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
[0038] Among them, the vehicle 110 can be any motor vehicle or non-motor vehicle.
[0039] In other possible implementation environments, Figure 1 the shown vehicle 110 can also communicate with an electronic device. The above pose determination method can be executed by the electronic device through interaction with the vehicle 110.
[0040] Among them, the electronic device obtains the first IMU data packet of the vehicle 110 at the current pose determination moment. The first IMU data packet includes multiple frames of target IMU data collected at different moments; the electronic device obtains the current pose information of the vehicle 110 at the current pose determination moment according to the first IMU data packet; before the electronic device obtains the second IMU data packet of the vehicle 110, based on the current pose information and the last frame of target IMU data in the first IMU data packet, the electronic device predicts the predicted pose information of the vehicle 110 at at least one prediction moment, and the prediction moment is the moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
[0041] Among them, the electronic device can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, portable wearable devices, etc.
[0042] Of course, in other possible implementation environments, Figure 1 the shown vehicle 110 can also communicate with a server, and the above pose determination method can be executed by the server through interaction with the vehicle 110, and so on.
[0043] In one embodiment, as Figure 2 shown, a pose determination method is provided. Taking the vehicle 110 in Figure 1 as an example for illustration, the method includes the following steps:
[0044] Step 201, the vehicle obtains the first IMU data packet of the vehicle at the current pose determination moment.
[0045] The pose determination moment refers to the moment when the IMU outputs the IMU data packet. The IMU in the vehicle outputs the IMU data packet at the current pose determination moment, and the vehicle obtains the IMU data packet. Here, the IMU data packet obtained by the vehicle at the current pose determination moment is called the first IMU data packet for distinction.
[0046] Among them, the first IMU data packet includes multiple frames of target IMU data collected at different moments.
[0047] If the first IMU data packet is the first IMU data packet output by the IMU during the current pose determination process, that is, the current pose determination moment is the first pose determination moment, then each of these different moments is the moment between the start moment of the current pose determination process and the current pose determination moment.
[0048] If the first IMU data packet is not the first IMU data packet output by the IMU during the current pose determination process, then each of these different moments is the moment between the current pose determination moment and the previous pose determination moment (i.e., the moment when the IMU last output the IMU data packet).
[0049] Exemplarily, assume that the current pose determination moment is t and the previous pose determination moment is t - 0.1s. Each of the above different moments can be, for example, t - 0.09s, t - 0.08s, t - 0.07s, t - 0.06s, t - 0.05s, t - 0.04s, t - 0.03s, t - 0.02s, and t - 0.01s. The multiple frames of target IMU data include the IMU data of the vehicle collected at each of these different moments.
[0050] In the embodiments of the present disclosure, optionally, the first IMU data packet may be the original MU data packet output by the IMU obtained by the vehicle at the current pose determination moment; optionally, the first IMU data packet may also be the IMU data packet obtained after processing the original IMU data packet output by the IMU obtained by the vehicle at the current pose determination moment. The processing may be, for example, removing invalid IMU data in the original IMU data packet, etc. That is, all the multiple frames of target IMU data included in the first IMU data packet are valid IMU data.
[0051] Step 202, the vehicle obtains the current pose information of the vehicle at the current pose determination moment according to the first IMU data packet.
[0052] The vehicle parses the first IMU data packet to obtain the multiple frames of target IMU data included in the first IMU data packet. The vehicle obtains the current pose information of the vehicle at the current pose determination moment according to the multiple frames of target IMU data.
[0053] Each frame of target IMU data may include a timestamp, acceleration, and angular velocity. The vehicle can obtain the pose information determined by the vehicle most recently. Then, the vehicle performs pose accumulation processing on the pose information determined most recently according to the multiple frames of target IMU data, and thus obtains the latest pose information of the vehicle at the current pose determination moment, that is, the current pose information.
[0054] As described above, the pose information includes the three-dimensional position and three-dimensional attitude of the vehicle, and the three-dimensional attitude may include the heading angle, pitch angle, etc. of the vehicle. Taking the heading angle as an example, based on the heading angle included in the most recently determined pose information of the vehicle, by using the timestamps of each frame of target IMU data and the three-axis angular velocity, the heading angle included in the most recently determined pose information is integrated, and then the latest heading angle of the vehicle at the current pose determination moment is obtained.
[0055] It should be noted that regarding the most recently determined pose information of the vehicle above, when the current pose determination moment is the first pose determination moment in the current pose determination process, the most recently determined pose information may refer to the initial pose of the vehicle; when the current pose determination moment is not the first pose determination moment in the current pose determination process, the most recently determined pose information may refer to the pose information determined by the vehicle at the previous pose determination moment.
[0056] Step 203, before the vehicle obtains the second IMU data packet of the vehicle, based on the current pose information and the last frame of target IMU data in the first IMU data packet, predict the predicted pose information of the vehicle at at least one prediction moment.
[0057] After the vehicle obtains the latest pose information of the vehicle, that is, the current pose information, according to the first IMU data packet, since it takes a period of time to receive the next packet of IMU data (i.e., the second IMU data packet). For example, if the output frequency of the IMU is 10Hz, the vehicle can receive the second IMU data packet after 100ms.
[0058] Considering that the IMU data of the vehicle changes little within a 100ms time window, in order to improve the output frequency of the pose information of the vehicle, in the embodiments of the present disclosure, after the current pose determination moment, the vehicle can perform pose prediction according to the last frame of target IMU data in the first IMU data packet and the current pose information, and obtain the predicted pose information of the vehicle at at least one prediction moment, where the prediction moment is the moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
[0059] As an implementation, during the process of predicting the predicted pose information of the vehicle, the vehicle can perform pose recursive prediction according to a preset recursive time period. Wherein, for each pose prediction, the vehicle performs pose prediction based on the last frame of target IMU data in the first IMU data packet and the latest pose information (for the first pose prediction, the latest pose information is the above-mentioned current pose information, and for subsequent pose predictions, the latest pose information is the predicted pose information obtained from the previous pose prediction), and obtains a predicted pose information. Wherein, the preset recursive time period is less than the time interval between the current pose determination moment and the next pose determination moment, and the preset recursive time period represents the time interval between two adjacent preset moments.
[0060] Exemplarily, referring to Figure 3 , Figure 3 is a timing diagram of an exemplary pose determination method.
[0061] Combined with Figure 3 , assuming that the output frequency of the IMU is 10Hz, that is, the time interval for the IMU to output the IMU data packet is 100ms, and the preset recursive time period is 10ms. The vehicle obtains the first IMU data packet IMU_Data(k) of the vehicle at the current pose determination moment t. Then, the vehicle determines the current pose information at the moment t as Pose(t) according to the IMU_Data(k).
[0062] Then, the vehicle performs pose prediction according to the recursive time period of 10ms, and obtains the predicted pose information Pose(t + 0.01*n). Theoretically, it is predicted 9 times in total, that is, the predicted pose information at t + 0.01s is Pose(t + 0.01), the predicted pose information at t + 0.02s is Pose(t + 0.02)...... the predicted pose information at t + 0.09s is Pose(t + 0.09).
[0063] When the moment of t + 0.1s arrives, the vehicle obtains the second IMU data packet IMU_Data(k + 1). Theoretically, IMU_Data(k + 1) includes 10 frames of IMU data at the moments of t + 0.1s, t + 0.09s, t + 0.08s...... t + 0.01s. The vehicle performs pose integration processing on all frames in IMU_Data(k + 1) based on Pose(t), and can obtain the accurate Pose(t + 0.1).
[0064] After that, repeat the process of step 203, that is, still perform the alternating update operation of prediction and pose integration after receiving the IMU data packet, and the output frequency of the pose information can be increased to 100Hz.
[0065] In a possible implementation, when the vehicle predicts the predicted pose information of the vehicle at at least one prediction moment based on the current pose information and the last frame of target IMU data in the first IMU data packet, it can also detect whether the second IMU data packet has not been obtained for more than a preset duration, that is, the vehicle performs control on the pose recursion time.
[0066] If the second IMU data packet has not been obtained for more than the preset duration, a second exception prompt message is output, and the prediction of the predicted pose information is prohibited from continuing.
[0067] The preset duration is, for example, 500 ms. Taking the output frequency of the IMU as 10 Hz, that is, the time interval for the IMU to output the IMU data packet is 100 ms as an example, 500 ms is the inter-packet interval of five IMU data packets.
[0068] During the process of the vehicle predicting the predicted pose information of at least one prediction moment, the total prediction time is recorded. If the second IMU data packet has not arrived after 500 ms, a data anomaly is reported.
[0069] In this way, the limit of the prediction time is increased to prevent the problem that the predicted pose information is too different from the actual motion state of the vehicle due to too long prediction time.
[0070] In the above embodiment, between the current pose determination moment and the next pose determination moment, the predicted pose information of at least one prediction moment is predicted. Compared with the traditional technology, when determining the pose information of the vehicle based on the IMU data, the pose information of the vehicle is only calculated at the moment when the IMU data packet is received, resulting in a low output frequency of the pose information and poor reliability of the pose determination. In the embodiment of the present disclosure, the determination of the predicted pose information is added between the current pose determination moment and the next pose determination moment, so that the output frequency of the pose information can be increased, the reliability of the pose determination can be improved, and it is beneficial to meet the requirements of vehicle control.
[0071] In one embodiment, based on Figure 2 the embodiment shown, see Figure 4 , this embodiment relates to the process of how the vehicle obtains the current pose information of the vehicle at the current pose determination moment according to the first IMU data packet. As Figure 4 shown, step 202 includes Figure 4 the steps 401 and 402 shown:
[0072] Step 401, the vehicle obtains the historical pose information of the vehicle at the previous pose determination moment.
[0073] As described above, the pose determination moment refers to the moment when the IMU outputs the IMU data packet, and the previous pose determination moment refers to the nearest moment when the IMU outputs the IMU data packet before the current pose determination moment.
[0074] Assume that the first IMU data packet is not the first IMU data packet output by the IMU during this pose determination process. It can be understood that at the previous pose determination moment, the vehicle can also obtain the IMU data packet output by the IMU at the previous pose determination moment (referred to as the third IMU data packet). Based on this third IMU data packet, the vehicle can obtain the historical pose information of the vehicle at the previous pose determination moment.
[0075] Among them, the process of the vehicle obtaining the historical pose information of the vehicle at the previous pose determination moment based on the third IMU data packet is similar to the process of the vehicle in this embodiment obtaining the current pose information of the vehicle at the current pose determination moment based on the first IMU data packet. For the implementation method, please refer to the relevant introduction of this embodiment.
[0076] Optionally, if the third IMU data packet is the first IMU data packet output by the IMU during this pose determination process, the vehicle performs pose accumulation processing on the initial pose of the vehicle according to multiple frames of IMU data included in the third IMU data packet, and then obtains the above-mentioned historical pose information.
[0077] Step 402: The vehicle performs pose accumulation processing based on the first IMU data packet and the historical pose information to obtain the current pose information.
[0078] The historical pose information is the pose information determined most recently for the current pose determination moment. The vehicle performs pose accumulation processing on the historical pose information according to multiple frames of target IMU data included in the first IMU data packet, and then obtains the current pose information.
[0079] In a possible implementation manner of step 402, the vehicle may sequentially perform integration processing on the first updated pose information based on each target IMU data in the order of the acquisition times of multiple frames of target IMU data to obtain the current pose information.
[0080] Among them, during the first integration processing, the first updated pose information is the historical pose information, and during the non-first integration processing, the first updated pose information is the pose information obtained from the previous integration processing.
[0081] That is, the vehicle can sequentially perform integration accumulation recursion using each target IMU data on the basis of the historical pose information determined based on the previous packet of IMU data packet, and obtain and output the latest current pose information.
[0082] Exemplarily, assume that the current pose determination time is t, and the previous pose determination time is t - 0.1 s. The multi-frame target IMU data includes the IMU data of the vehicle collected at t - 0.09 s, t - 0.08 s, t - 0.07 s, t - 0.06 s, t - 0.05 s, t - 0.04 s, t - 0.03 s, t - 0.02 s, and t - 0.01 s. In this way, in the order of the acquisition times of the multi-frame target IMU data, the vehicle first uses the target IMU data corresponding to t - 0.09 s to perform integration processing on the historical pose information (i.e., the pose information corresponding to the previous pose determination time t - 0.1 s) to obtain the pose information corresponding to t - 0.09 s. Then, the vehicle uses the target IMU data corresponding to t - 0.08 s to perform integration processing on the pose information corresponding to t - 0.09 s to obtain the pose information corresponding to t - 0.08 s, and so on. Finally, the vehicle uses the target IMU data corresponding to time t to perform integration processing on the pose information corresponding to t - 0.01 s to obtain the latest current pose information.
[0083] In another possible implementation manner of step 402, the vehicle may also calculate the pose information change amount according to each target IMU data included in the first IMU data packet, and then synchronously accumulate each pose information change amount in the historical pose information to obtain the current pose information.
[0084] Through the above implementation manners in this embodiment, the vehicle can obtain and output the accurate current pose information of the vehicle at the current pose determination time according to the first IMU data packet, providing an accurate data basis for the subsequent pose prediction and being beneficial to improving the accuracy of the pose prediction.
[0085] In one embodiment, based on Figure 2 the embodiment shown, refer to Figure 5 , this embodiment relates to the process of how the vehicle predicts the predicted pose information of the vehicle at at least one prediction time based on the current pose information and the last frame of target IMU data in the first IMU data packet. As Figure 5 shown, step 203 includes Figure 5 the step 501 shown in
[0086] Step 501, before obtaining the second IMU data packet of the vehicle, based on the last frame of target IMU data, perform pose recursion processing on the second updated pose information according to a preset recursion time period to obtain each predicted pose information.
[0087] Among them, in the process of the first pose recursion processing, the second updated pose information is the current pose information, and in the process of non-first pose recursion processing, the second updated pose information is the pose information obtained from the previous pose recursion processing.
[0088] The second IMU data packet is the IMU data packet output by the IMU at the next moment of outputting an IMU data packet (i.e., the next pose determination moment). In order to increase the output frequency of pose information, pose information should be output within the packet interval between adjacent IMU data packets.
[0089] In the embodiments of the present disclosure, after the vehicle obtains the current pose information of the vehicle at the current pose determination moment according to the first IMU data packet, and before obtaining the second IMU data packet of the vehicle, that is, within the packet interval between the first IMU data packet and the second IMU data packet, the vehicle performs pose prediction according to a preset recursive time period, obtains a plurality of predicted pose information within the packet interval between the first IMU data packet and the second IMU data packet, and outputs them.
[0090] Exemplarily, assuming that the recursive time period is 10 ms, the vehicle can perform pose recursive prediction at a time interval of 10 ms based on the last frame of target IMU data in the first IMU data packet and the latest pose information (i.e., the current pose information), and output the predicted pose information.
[0091] For example, the current pose information is the pose information at time t. At time t + 0.01 s, the vehicle uses the last frame of target IMU data (the last frame of target IMU data is the IMU data with the most recent time) in the first IMU data packet at time t to perform integral processing on the current pose information to obtain the pose information corresponding to t + 0.01 s. Then, at time t + 0.02 s, the vehicle continues to use the above last frame of target IMU data to perform integral processing on the pose information corresponding to t + 0.01 s to obtain the pose information corresponding to t + 0.02 s, and so on.
[0092] If at time t + 0.09 s, the vehicle continues to use the above last frame of target IMU data to perform integral processing on the pose information corresponding to t + 0.08 s to obtain the pose information corresponding to t + 0.09 s, and then at time t + 0.1 s, the vehicle obtains the second IMU data packet, the vehicle takes the second IMU data packet as the first IMU data packet at the current pose determination moment and re-enters the process of step 201 of the embodiments of the present disclosure. In this way, pose accumulation is performed after receiving the IMU data packet, accurate pose information is calculated, and within the packet interval between two adjacent IMU data packets, a plurality of predicted pose information is predicted and output, improving the output frequency of pose information, thereby improving the reliability of pose determination.
[0093] In one embodiment, based on any of the above embodiments, refer to Figure 6 This embodiment relates to the process of the vehicle obtaining the first IMU data packet of the vehicle at the current pose determination moment. As Figure 6As shown, step 201 includes Figure 6 Steps 601 to 603 shown:
[0094] Step 601, obtaining a candidate IMU data packet of the vehicle at the moment of current posture determination.
[0095] The candidate IMU data packet includes multiple frames of candidate IMU data collected at different times.
[0096] The candidate IMU data packet refers to the original MU data packet output by the IMU obtained by the vehicle at the moment of current posture determination.
[0097] The above embodiments all assume that the output frequency of the IMU is 10 Hz, each IMU data packet includes 10 frames of IMU data, and the interval between two adjacent IMU data packets is 100 ms. However, in actual applications, the number of valid frames in each IMU data packet may not be 10 frames, and the interval between packets may also exceed 100 ms. Therefore, when the vehicle receives each IMU data packet, it is necessary to check the valid frames of each IMU data packet.
[0098] Step 602: Determine the collection time interval between two adjacent frames of candidate IMU data according to the candidate IMU data packets.
[0099] Each frame of candidate IMU data may include a timestamp, acceleration, and angular velocity. Therefore, the vehicle can determine the collection time interval between two adjacent frames of candidate IMU data based on the timestamps of the two adjacent frames of candidate IMU data.
[0100] Step 603: According to each collection time interval, invalid candidate IMU data is removed from the candidate IMU data packets to obtain a first IMU data packet.
[0101] In a possible implementation of step 603, if the collection time interval between two adjacent frames of candidate IMU data in the candidate IMU data packet is less than a first threshold, one frame of candidate IMU data is removed from the two adjacent frames of candidate IMU data to obtain a first IMU data packet.
[0102] In another possible implementation of step 603, when the collection time interval between two adjacent frames of candidate IMU data is less than the first threshold, after the vehicle eliminates one frame of candidate IMU data from two adjacent frames of candidate IMU data, the vehicle continues to repeat the above process for the eliminated candidate IMU data packet, determines the collection time interval between two adjacent frames of candidate IMU data to eliminate invalid candidate IMU data, until the collection time interval between two adjacent frames of candidate IMU data is not less than the first threshold, and the first IMU data packet is obtained.
[0103] The first threshold can be, for example, 10 ms. When the vehicle checks the timestamp of the IMU data for each candidate frame, it determines whether the timestamp has increased by around 10 ms compared to the previous candidate IMU data frame. The candidate IMU data with abnormal timestamps needs to be discarded.
[0104] If the acquisition time interval between two adjacent candidate IMU data frames in the candidate IMU data packet is greater than the second threshold, a first abnormal prompt message is output, and the acquisition of the current pose information is prohibited. The second threshold is greater than the first threshold.
[0105] The second threshold can be, for example, 50 ms. When the vehicle performs data validity checks on the candidate IMU data packet, the acquisition time interval (or the inter-frame time interval) between two adjacent candidate IMU data frames is not allowed to regress or increase by more than 50 ms. The data frames with regression are directly discarded, and the cases where the increase exceeds 50 ms are reported as data anomalies, and the current process is exited.
[0106] In this way, for the valid data frames screened out by the present disclosure embodiment, that is, the multiple target IMU data frames included in the first IMU data packet, the vehicle then performs pose accumulation processing based on the most recently determined pose information, and thus obtains the latest pose information at the current pose determination moment, that is, the current pose information and outputs it.
[0107] The pose determination method of the present disclosure embodiment is an upsampling method for the continuous frame data of a low-cost IMU (that is, each IMU data packet output by the IMU includes multiple frames of IMU data, and the IMU data collected by the IMU is cumulatively output), which is used to solve the problem of output downsampling caused by the IMU continuous frame sending mechanism, and thus the core advantages of the IMU cannot be exerted. The present disclosure embodiment can effectively increase the output frequency of the pose information. For example, the output frequency of the pose information can be increased to 100 Hz, so that the positioning output frequency can be increased to 100 Hz, meeting the frequency index requirements of the downstream modules of the intelligent driving system for the positioning output to satisfy the functional applications of intelligent driving.
[0108] Exemplarily, referring to Figure 7 , Figure 7 is the experimental effect diagram of the present disclosure embodiment taking the heading angle output as an example. The RTK (Real Time Kinematic) reference heading angle is the reference value of the heading angle. Figure 7 In the figure (1), the curve of the x86 offline output heading angle is the curve of the heading angle output by the traditional method, and there are obvious steps in the heading angle output. Figure 7 In the figure (2), the curve of the x86 offline output heading angle is the curve of the heading angle output by the pose determination method of the present disclosure embodiment, and the steps in the heading angle output are significantly improved.
[0109] In one embodiment, a pose determination method is provided. Refer to Figure 8 As shown, the method includes the following steps:
[0110] Step 801, the vehicle obtains candidate IMU data packets of the vehicle at the current pose determination moment.
[0111] In this embodiment, the output frequency of the IMU is 10 Hz, that is, the time interval for the IMU to output IMU data packets is 100 ms as an example.
[0112] Assume that the current pose determination moment is assumed to be moment t. The candidate IMU data packets include multiple frames of candidate IMU data collected at multiple different moments between t - 0.1 s and the moment.
[0113] Step 802, the vehicle determines the acquisition time interval between two adjacent frames of candidate IMU data according to the candidate IMU data packets.
[0114] Each frame of candidate IMU data includes a timestamp, acceleration, and angular velocity. The vehicle can determine the acquisition time interval between two adjacent frames of candidate IMU data according to the timestamps of the two adjacent frames of candidate IMU data.
[0115] Step 803, if the acquisition time interval between two adjacent frames of candidate IMU data in the candidate IMU data packets is less than the first threshold, the vehicle eliminates one frame of candidate IMU data from the two adjacent frames of candidate IMU data to obtain the first IMU data packet.
[0116] Step 804, if the acquisition time interval between two adjacent frames of candidate IMU data in the candidate IMU data packets is greater than the second threshold, the vehicle outputs the first abnormal prompt information and prohibits continuing to obtain the current pose information.
[0117] The second threshold is greater than the first threshold. The first threshold can be, for example, 10 ms, and the second threshold can be, for example, 50 ms. When the vehicle performs data validity check on the candidate IMU data packets, the acquisition time interval between two adjacent frames of candidate IMU data is not allowed to have a rollback or increase of more than 50 ms. The data frames with rollback are directly discarded, and the situation where the increase exceeds 50 ms is reported as data abnormality and the current process is exited.
[0118] Step 805, the vehicle obtains the historical pose information of the vehicle at the previous pose determination moment.
[0119] The previous pose determination moment refers to the nearest moment when the IMU outputs an IMU data packet before the current pose determination moment. The historical pose information is obtained by performing pose cumulative processing calculation based on the third IMU data packet output by the vehicle based on the IMU at the previous pose determination moment.
[0120] Step 806: The vehicle sequentially performs integration processing on the first updated pose information based on each piece of target IMU data in the order of the acquisition times of multiple frames of target IMU data to obtain the current pose information.
[0121] Among them, during the first integration process, the first updated pose information is the historical pose information, and during the non-first integration process, the first updated pose information is the pose information obtained from the previous integration process.
[0122] Among them, the current pose determination time is t, the previous pose determination time is t - 0.1s, and the multiple frames of target IMU data include the IMU data of the vehicle collected at t - 0.09s, t - 0.08s, t - 0.07s, t - 0.06s, t - 0.05s, t - 0.04s, t - 0.03s, t - 0.02s, and t - 0.01s.
[0123] The vehicle first performs integration processing on the historical pose information (i.e., the pose information corresponding to the previous pose determination time t - 0.1s) using the target IMU data corresponding to t - 0.09s to obtain the pose information corresponding to t - 0.09s. Then, the vehicle performs integration processing on the pose information corresponding to t - 0.09s using the target IMU data corresponding to t - 0.08s to obtain the pose information corresponding to t - 0.08s, and so on. Finally, the vehicle performs integration processing on the pose information corresponding to t - 0.01s using the target IMU data corresponding to t to obtain the latest current pose information.
[0124] Step 807: Before obtaining the second IMU data packet of the vehicle, the vehicle performs pose recursion processing on the second updated pose information based on the last frame of target IMU data according to a preset recursion time period to obtain each predicted pose information.
[0125] Among them, during the first pose recursion process, the second updated pose information is the current pose information, and during the non-first pose recursion process, the second updated pose information is the pose information obtained from the previous pose recursion process. The prediction time is the time between the current pose determination time and the next pose determination time corresponding to the second IMU data packet.
[0126] The current pose information is the pose information at time t. At time t + 0.01 s, the vehicle uses the last frame of target IMU data (the last frame of target IMU data is the IMU data with the most recent time) in the first IMU data packet at time t to perform integral processing on the current pose information to obtain the pose information corresponding to t + 0.01 s. Then, at time t + 0.02 s, the vehicle continues to use the above-mentioned last frame of target IMU data to perform integral processing on the pose information corresponding to t + 0.01 s to obtain the pose information corresponding to t + 0.02 s, and so on.
[0127] If at time t + 0.09 s, the vehicle continues to use the above-mentioned last frame of target IMU data to perform integral processing on the pose information corresponding to t + 0.08 s to obtain the pose information corresponding to t + 0.09 s, and then at time t + 0.1 s, if the vehicle obtains a second IMU data packet, the vehicle will use the second IMU data packet as a candidate IMU data packet obtained at the current pose determination moment and re-enter the process of step 801 of the present disclosure embodiment. In this way, after receiving the IMU data packet, pose accumulation is performed to calculate accurate pose information, and within the packet interval between two adjacent IMU data packets, multiple predicted pose information is predicted and output, improving the output frequency of the pose information, thereby improving the reliability of pose determination.
[0128] Step 808, if the second IMU data packet is not obtained after exceeding the preset duration, the vehicle outputs a second exception prompt message and prohibits the continued prediction of the predicted pose information.
[0129] During the process of the vehicle predicting the predicted pose information with one less prediction moment, the limit of the prediction time is increased. If there is still no arrival of the second IMU data packet after exceeding the preset duration (for example, 500 ms), data abnormality is reported, thereby preventing the problem that the predicted pose information is too different from the actual motion state of the vehicle due to too long prediction time.
[0130] It should be understood that although the steps in the above flow chart are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above flow chart may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.
[0131] In one embodiment, as Figure 9 shown, a pose determination device is provided, including:
[0132] An acquisition module 901, configured to acquire a first IMU data packet of a vehicle at a current pose determination moment, where the first IMU data packet includes multiple frames of target IMU data collected at different moments;
[0133] A pose determination module 902, configured to obtain current pose information of the vehicle at the current pose determination moment according to the first IMU data packet;
[0134] A pose prediction module 903, configured to, before acquiring a second IMU data packet of the vehicle, predict predicted pose information of the vehicle at at least one prediction moment based on the current pose information and the last frame of target IMU data in the first IMU data packet, where the prediction moment is a moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
[0135] In one embodiment, the pose determination module 902 includes:
[0136] A first acquisition unit, configured to acquire historical pose information of the vehicle at a previous pose determination moment;
[0137] A pose accumulation unit, configured to perform pose accumulation processing according to the first IMU data packet and the historical pose information to obtain the current pose information.
[0138] In one embodiment, the pose accumulation unit is specifically configured to sequentially perform integration processing on first updated pose information based on each of the target IMU data in the order of the acquisition moments of the multiple frames of target IMU data to obtain the current pose information; where, in the process of the first integration processing, the first updated pose information is the historical pose information, and in the process of non-first integration processing, the first updated pose information is the pose information obtained by the previous integration processing.
[0139] In one embodiment, the pose prediction module 903 is specifically configured to perform pose recursion processing on second updated pose information based on the last frame of target IMU data according to a preset recursion time period to obtain each of the predicted pose information; where, in the process of the first pose recursion processing, the second updated pose information is the current pose information, and in the process of non-first pose recursion processing, the second updated pose information is the pose information obtained by the previous pose recursion processing.
[0140] In one embodiment, the acquisition module 901 includes:
[0141] A second acquisition unit, configured to acquire a candidate IMU data packet of the vehicle at the current pose determination moment, where the candidate IMU data packet includes multiple frames of candidate IMU data collected at different moments;
[0142] A determination unit, configured to determine an acquisition time interval between two adjacent frames of candidate IMU data according to the candidate IMU data packet;
[0143] An elimination unit, configured to eliminate invalid candidate IMU data from the candidate IMU data packet according to each of the acquisition time intervals to obtain the first IMU data packet.
[0144] In one embodiment, the elimination unit is specifically configured to, if the acquisition time interval between two adjacent frames of candidate IMU data in the candidate IMU data packet is less than a first threshold, eliminate one frame of candidate IMU data from the two adjacent frames of candidate IMU data to obtain the first IMU data packet.
[0145] In one embodiment, the acquisition module 901 further includes:
[0146] A prompt unit, configured to, if the acquisition time interval between two adjacent frames of candidate IMU data in the candidate IMU data packet is greater than a second threshold, output a first exception prompt message and prohibit continued acquisition of the current pose information, where the second threshold is greater than the first threshold.
[0147] In one embodiment, the apparatus further includes:
[0148] A prompt module, configured to, if the second IMU data packet is not acquired within a preset duration, output a second exception prompt message and prohibit continued prediction of the predicted pose information.
[0149] For the specific definition of the pose determination device, reference may be made to the definition of the pose determination method in the foregoing text, which will not be elaborated here. Each module in the foregoing pose determination device may be implemented in whole or in part by software, hardware, and their combination. The foregoing modules may be embedded in a processor in the electronic device in a hardware form or be independent of the processor, or may be stored in a memory in the electronic device in a software form, so that the processor can call and execute the operations corresponding to the foregoing modules.
[0150] Figure 10 is a block diagram of an electronic device shown according to an exemplary embodiment. For example, the electronic device 1300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a personal digital assistant, a vehicle-mounted device (such as a vehicle-mounted control device), etc.
[0151] Refer to Figure 10, the electronic device 1300 may include one or more of the following components: a processing component 1302, a memory 1304, a power supply component 1306, a multimedia component 1308, an audio component 1310, an input / output (I / O) interface 1312, a sensor component 1314, and a communication component 1316. Among them, a computer program or instruction that runs on the processor is stored on the memory.
[0152] The processing component 1302 generally controls the overall operation of the electronic device 1300, such as operations associated with display, telephone call, data communication, camera operation, and recording operation. The processing component 1302 may include one or more processors 1320 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 1302 may include one or more modules to facilitate the interaction between the processing component 1302 and other components. For example, the processing component 1302 may include a multimedia module to facilitate the interaction between the multimedia component 1308 and the processing component 1302.
[0153] The memory 1304 is configured to store various types of data to support the operation of the electronic device 1300. Examples of such data include instructions for any application or method operating on the electronic device 1300, contact data, phone book data, messages, pictures, videos, etc. The memory 1304 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk.
[0154] The power supply component 1306 provides power to various components of the electronic device 1300. The power supply component 1306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1300.
[0155] The multimedia component 1308 includes a touch display screen that provides an output interface between the electronic device 1300 and the user. In some embodiments, the touch display screen may include a liquid crystal display (LCD) and a touch panel (TP). The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of a touch or swipe action but also detect the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 1308 includes a front camera and / or a rear camera. When the electronic device 1300 is in an operation mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each of the front camera and the rear camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.
[0156] The audio component 1310 is configured to output and / or input audio signals. For example, the audio component 1310 includes a microphone (MIC) that is configured to receive external audio signals when the electronic device 1300 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals can be further stored in the memory 1304 or transmitted via the communication component 1316. In some embodiments, the audio component 1310 further includes a speaker for outputting audio signals.
[0157] The I / O interface 1312 provides an interface between the processing component 1302 and a peripheral interface module, and the peripheral interface module can be a keyboard, a click wheel, buttons, etc. These buttons can include but are not limited to: a home button, a volume button, a power button, and a lock button.
[0158] The sensor component 1314 includes one or more sensors for providing an assessment of the status of various aspects of the electronic device 1300. For example, the sensor component 1314 can detect the on / off state of the electronic device 1300, the relative positioning of components, such as the display and the keypad of the electronic device 1300. The sensor component 1314 can also detect a change in the position of the electronic device 1300 or a component of the electronic device 1300, the presence or absence of user contact with the electronic device 1300, the orientation or acceleration / deceleration of the electronic device 1300, and the temperature change of the electronic device 1300. The sensor component 1314 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 1314 can also include a light sensor, such as a CMOS or a CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 1314 can further include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
[0159] The communication component 1316 is configured to facilitate communication between the electronic device 1300 and other devices in a wired or wireless manner. The electronic device 1300 can access a communication standard-based wireless network, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1316 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra-Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
[0160] In an exemplary embodiment, the electronic device 1300 can be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-mentioned pose determination method.
[0161] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1304 including instructions, and the above instructions can be executed by a processor 1320 of the electronic device 1300 to complete the above method. For example, the non-transitory computer-readable storage medium can be a ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0162] In an exemplary embodiment, a computer program product is also provided. When the computer program is executed by a processor, the above method can be implemented. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, part or all of the above method can be implemented in accordance with the process or function described in the embodiments of the present disclosure.
[0163] It should be noted that for the solutions described in this specification and embodiments, if they involve personal information processing, they will be processed on the premise of having a legal basis (such as obtaining the consent of the personal information subject, or being necessary for performing a contract, etc.), and will only be processed within the specified or agreed scope. If a user refuses to process personal information other than the necessary information required for basic functions, it will not affect the user's use of basic functions.
[0164] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0165] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.
[0166] The above-described embodiments merely represent several implementation manners of the embodiments of the present disclosure. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present disclosure, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present disclosure. Therefore, the protection scope of the patent of the embodiments of the present disclosure should be subject to the appended claims.
Claims
1. A pose determination method, characterized in that, The method includes: Obtaining a first IMU data packet of the vehicle at the current pose determination moment, where the first IMU data packet includes multiple frames of target IMU data collected at different moments; Obtaining the current pose information of the vehicle at the current pose determination moment according to the first IMU data packet; Before obtaining the second IMU data packet of the vehicle, predicting the predicted pose information of the vehicle at at least one prediction moment based on the current pose information and the last frame of target IMU data in the first IMU data packet, where the prediction moment is the moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
2. The method according to claim 1, wherein The obtaining the current pose information of the vehicle at the current pose determination moment according to the first IMU data packet includes: Obtaining the historical pose information of the vehicle at the previous pose determination moment; Performing pose accumulation processing according to the first IMU data packet and the historical pose information to obtain the current pose information.
3. The method according to claim 2, wherein The performing pose accumulation processing according to the first IMU data packet and the historical pose information to obtain the current pose information includes: Sequentially performing integration processing on the first updated pose information based on each of the target IMU data in the order of the acquisition moments of the multiple frames of target IMU data to obtain the current pose information; Wherein, during the first integration processing, the first updated pose information is the historical pose information, and during the non-first integration processing, the first updated pose information is the pose information obtained from the previous integration processing.
4. The method according to claim 1, wherein The predicting the predicted pose information of the vehicle at at least one prediction moment based on the current pose information and the last frame of target IMU data in the first IMU data packet includes: Performing pose recursion processing on the second updated pose information based on the last frame of target IMU data according to a preset recursion time period to obtain each of the predicted pose information; Wherein, during the first pose recursion processing, the second updated pose information is the current pose information, and during the non-first pose recursion processing, the second updated pose information is the pose information obtained from the previous pose recursion processing.
5. The method according to any one of claims 1-4, characterized in that, The obtaining the first IMU data packet of the vehicle at the current pose determination moment includes: Obtaining a candidate IMU data packet of the vehicle at the current pose determination moment, where the candidate IMU data packet includes multiple frames of candidate IMU data collected at different moments; Determining the acquisition time interval between two adjacent frames of candidate IMU data according to the candidate IMU data packet; Eliminating invalid candidate IMU data from the candidate IMU data packet according to each of the acquisition time intervals to obtain the first IMU data packet.
6. The method according to claim 5, wherein The eliminating invalid candidate IMU data from the candidate IMU data packet according to each of the acquisition time intervals to obtain the first IMU data packet includes: If the acquisition time interval between two adjacent frames of candidate IMU data in the candidate IMU data packet is less than a first threshold, one frame of candidate IMU data is removed from the two adjacent frames of candidate IMU data to obtain the first IMU data packet.
7. The method according to claim 6, wherein The method further includes: If the acquisition time interval between two adjacent frames of candidate IMU data in the candidate IMU data packet is greater than a second threshold, a first exception prompt message is output, and continuing to obtain the current pose information is prohibited, where the second threshold is greater than the first threshold.
8. The method according to claim 1, wherein The method further includes: If the second IMU data packet is not obtained within a preset duration, a second exception prompt message is output, and continuing to predict the predicted pose information is prohibited.
9. A pose determination device, characterized in that, The device includes: An acquisition module, configured to acquire a first IMU data packet of a vehicle at a current pose determination moment, where the first IMU data packet includes multiple frames of target IMU data acquired at different moments; A pose determination module, configured to obtain the current pose information of the vehicle at the current pose determination moment according to the first IMU data packet; A pose prediction module, configured to, before the second IMU data packet of the vehicle is acquired, predict the predicted pose information of the vehicle at at least one prediction moment based on the current pose information and the last frame of target IMU data in the first IMU data packet, where the prediction moment is a moment between the current pose determination moment and the next pose determination moment corresponding to the second IMU data packet.
10. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.
11. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.
12. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.