A fusion positioning method, electronic device, storage medium and program product

Abstract

The application discloses a fusion positioning method, an electronic device, a storage medium and a program product. The method comprises the following steps: acquiring a coordinate system conversion relationship when an object is aligned in a satellite positioning and laser positioning; converting satellite positioning data and laser positioning data of the object at a current time into a unified coordinate system according to the coordinate system conversion relationship to obtain an observation pose coordinate of the object at the current time; determining a confidence degree of a satellite positioning result according to parameter information when the object adopts satellite positioning, and determining a confidence degree of a laser positioning result according to parameter information when the object adopts laser positioning; determining an observation fusion noise according to the confidence degree of the satellite positioning result, the confidence degree of the laser positioning result, laser measurement noise and satellite measurement noise; and determining a fusion pose coordinate of the object at the current time according to the observation fusion noise, a state covariance matrix of object positioning, the observation pose coordinate and a predicted pose coordinate, so that accurate and continuous positioning of the object is realized, and positioning smoothness is improved.

Description

Technical Field

[0001] This invention relates to the field of navigation and positioning technology, and in particular to a fusion positioning method, electronic device, storage medium, and program product. Background Technology

[0002] With the development of technology, navigation and positioning are becoming increasingly common in daily life, such as in vehicle and robot navigation. In navigation and positioning, the positioning scenarios have gradually expanded from single indoor or outdoor scenarios to continuous cross-scenario operating environments. For example, logistics delivery robots, emergency rescue robots, inspection robots, and service robots often need to operate continuously between inside buildings, semi-open spaces, and outdoor roads, placing higher demands on the accuracy, stability, and continuity of the positioning system in different environments.

[0003] Among existing positioning technologies, laser positioning and Global Navigation Satellite System (GNSS) have irreplaceable technological advantages in indoor and outdoor environments, respectively. However, their environmental adaptability and coordinate systems differ.

[0004] For example, in indoor environments, due to complex building structures and dense reinforced concrete and metal facilities, satellite signals are easily severely blocked and affected by multipath effects, resulting in significant attenuation or even complete loss of GNSS signal strength, making it impossible to provide continuous and reliable positioning information. In such environments, laser positioning, by sensing stable geometric features such as walls, columns, and equipment, and comparing them with a pre-built high-precision map using point cloud matching algorithms, can achieve centimeter-level positioning accuracy even without satellite signals. It is also unaffected by electromagnetic interference and exhibits high positioning stability, making it particularly suitable for indoor, underground, and semi-enclosed spaces. Therefore, in indoor scenarios, laser positioning is irreplaceable and a key foundation for achieving high-precision autonomous navigation and control of robots.

[0005] In outdoor scenarios, environmental characteristics change significantly. Outdoor scenes include large open areas, vegetation cover, varying road surface reflections, and numerous dynamic targets. This reduces the stable geometric features that lidar can detect, and point cloud distribution varies considerably with lighting, season, and environment, making it prone to feature degradation. Under these conditions, lidar point cloud-based matching algorithms are susceptible to convergence instability, increased matching errors, and even matching failures, leading to fluctuating positioning results and even trajectory drift. In contrast, GNSS positioning provides stable absolute geographic location information in open outdoor environments, is independent of environmental geometric features, has wide coverage, and good long-term stability, exhibiting a significant advantage, especially in large-scale scenarios.

[0006] Therefore, existing technologies typically employ laser positioning for indoor positioning and GNSS positioning for outdoor positioning. However, in cross-scene scenarios, such as transitioning from indoor to outdoor or vice versa, continuous and accurate positioning is often difficult to achieve. Therefore, there is an urgent need to provide a fusion positioning method to achieve smooth positioning during multi-scene transitions, avoiding instantaneous changes in positioning results that could cause objects such as robots to stop abruptly or veer off course. Summary of the Invention

[0007] This invention provides a fusion positioning method, electronic device, storage medium, and program product to achieve smooth, continuous, and accurate positioning during multi-scene switching.

[0008] According to one aspect of the present invention, a fusion positioning method is provided, the method comprising: Obtain the coordinate system transformation relationship when aligning an object's coordinate system using satellite positioning and laser positioning; Based on the coordinate system transformation relationship, the satellite positioning data and laser positioning data of the object at the current moment are transformed into a unified coordinate system to obtain the observed pose coordinates of the object at the current moment; The confidence level of the satellite positioning result is determined based on the parameter information of the object when satellite positioning is used, and the confidence level of the laser positioning result is determined based on the parameter information of the object when laser positioning is used. The observation fusion noise is determined based on the confidence level of the satellite positioning results, the confidence level of the laser positioning results, the laser measurement noise, and the satellite measurement noise. The fused pose coordinates of the object at the current moment are determined based on the observed fusion noise, the state covariance matrix of the object localization, the observed pose coordinates, and the predicted pose coordinates.

[0009] According to another aspect of the present invention, a fusion positioning device is provided, the device comprising: The coordinate system transformation relationship acquisition module is used to acquire the coordinate system transformation relationship of an object when aligning its coordinate system with that of satellite positioning and laser positioning. The observation pose coordinate determination module is used to convert the satellite positioning data and laser positioning data of the object at the current moment to a unified coordinate system according to the coordinate system transformation relationship, so as to obtain the observation pose coordinates of the object at the current moment. The confidence level determination module for positioning results is used to determine the confidence level of satellite positioning results based on the parameter information when the object is positioned by satellite, and to determine the confidence level of laser positioning results based on the parameter information when the object is positioned by laser. The observation fusion noise determination module is used to determine the observation fusion noise based on the confidence level of the satellite positioning result, the confidence level of the laser positioning result, the laser measurement noise, and the satellite measurement noise. The fusion pose coordinate determination module is used to determine the fusion pose coordinates of the object at the current moment based on the observation fusion noise, the state covariance matrix of the object localization, the observed pose coordinates, and the predicted pose coordinates.

[0010] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the fusion positioning method according to any embodiment of the present invention.

[0011] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the fusion positioning method according to any embodiment of the present invention.

[0012] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the fusion positioning method described in any embodiment of the present invention.

[0013] The technical solution of this invention obtains the coordinate system transformation relationship when aligning the coordinate systems of an object during satellite positioning and laser positioning; transforms the current satellite positioning data and laser positioning data of the object to a unified coordinate system according to the coordinate system transformation relationship to obtain the observed pose coordinates of the object at the current moment; determines the confidence level of the satellite positioning result based on the parameter information of the object when using satellite positioning, and determines the confidence level of the laser positioning result based on the parameter information of the object when using laser positioning; determines the observation fusion noise based on the confidence level of the satellite positioning result, the confidence level of the laser positioning result, the laser measurement noise, and the satellite measurement noise; and determines the fused pose coordinates of the object at the current moment based on the observation fusion noise, the state covariance matrix of the object positioning, the observed pose coordinates, and the predicted pose coordinates. This solves the problem of inaccurate positioning when positioning across scenes. By considering coordinate system unity, the confidence level of the positioning result, and the fusion of measurement errors in positioning fusion, accurate and continuous positioning of the object is achieved, improving the smoothness of positioning when switching between multiple scenes.

[0014] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a fusion positioning method provided according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of a fusion positioning method provided according to Embodiment 2 of the present invention; Figure 3 This is a flowchart of another fusion positioning method provided according to Embodiment 2 of the present invention; Figure 4 This is a schematic diagram of the structure of a fusion positioning device according to Embodiment 3 of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device that implements the fusion positioning method of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] Example 1 Figure 1This is a flowchart of a fusion positioning method according to Embodiment 1 of the present invention. This embodiment is applicable to situations where positioning fusion is performed during scene switching, such as when a robot moves from indoors to outdoors or from outdoors to indoors, and the satellite positioning and laser positioning results are fused. This method can be executed by a fusion positioning device, which can be implemented in hardware and / or software. The fusion positioning device can be configured in an electronic device, such as a robot, vehicle, drone, mobile phone, computer, wearable device, and controller, etc. Figure 1 As shown, the method includes: Step 110: Obtain the coordinate system transformation relationship when aligning the coordinate systems of the object in satellite positioning and laser positioning.

[0020] The object can be a robot, vehicle, drone, wearable device, mobile phone, or computer, or any other device requiring positioning. The object can be positioned by fusing satellite and laser positioning results during multi-scene transitions, such as switching from indoors to outdoors or vice versa. Satellite positioning is based on the global geodetic coordinate system. Laser positioning is based on the object's initial pose or a local coordinate system used in map construction. Satellite positioning and laser positioning differ in their coordinate systems. Satellite positioning can integrate a receiver, high-gain antenna, and anti-interference unit, and uses one or more of the BeiDou, GPS, Galileo, or GLONASS satellite navigation systems for calculation, obtaining real-time object pose data. Laser positioning can be achieved using a 32-line lidar, coupled with a preprocessing unit and a Normal Distribution Transform (NDT) matching calculation unit. For example, the raw point cloud data collected by the lidar is preprocessed (such as denoising, downsampling, distortion correction, etc.) to remove dynamic obstacle point clouds and noisy point clouds, reducing the amount of subsequent matching calculations. Based on a preset global point cloud map, the preprocessed real-time point cloud is registered with the map point cloud through the NDT matching algorithm, and the three-dimensional pose information of the object in the local coordinate system of the lidar is output.

[0021] To ensure positioning continuity during multi-scene transitions and avoid breakpoints or jumps that could affect object stability and safety, a unified coordinate system can be used for both satellite and laser positioning during positioning fusion. This unified coordinate system can be achieved by transforming satellite positioning data to the laser positioning coordinate system, or by transforming laser positioning data to the satellite positioning coordinate system, or by transforming both satellite and laser positioning data to the target coordinate system. For example, the coordinate systems of satellite and laser positioning can be aligned using coordinate system calibration or singular value decomposition, resulting in a coordinate system transformation relationship. During real-time object movement, coordinate system alignment can be performed based on the acquired positioning data to obtain the transformation relationship. Alternatively, the object's positioning data in the scene can be pre-acquired, coordinate system alignment performed, and the transformation relationship used during real-time positioning to achieve the unified coordinate system for both satellite and laser positioning.

[0022] Step 120: Based on the coordinate system transformation relationship, convert the satellite positioning data and laser positioning data of the object at the current moment to a unified coordinate system to obtain the observation pose coordinates of the object at the current moment.

[0023] For example, the observed pose coordinates of the object at the current moment are: ,or .in, Let k be the laser positioning pose in the unified coordinate system at time k. The satellite positioning pose in the unified coordinate system at time k. Let k be the laser positioning coordinates in the unified coordinate system at time k. Let k be the satellite positioning coordinates in the unified coordinate system at time k. Let be the heading angle of the object during laser positioning in the unified coordinate system at time k. Let k be the heading angle of the object when it is satellite-positioned in the unified coordinate system at time k.

[0024] By converting satellite positioning data and laser positioning data to a unified coordinate system, the pose data in the two coordinate systems can be unified, providing a compatible data foundation for subsequent adaptive weighted fusion positioning data and avoiding fusion positioning deviations caused by coordinate system differences.

[0025] Step 130: Determine the confidence level of the satellite positioning result based on the parameter information of the object when using satellite positioning, and determine the confidence level of the laser positioning result based on the parameter information of the object when using laser positioning.

[0026] The confidence level of satellite positioning results can be considered as the reliability of satellite positioning. The parameters used in satellite positioning can include one or more of the following: positioning solution state, number of satellites, and positioning accuracy. The confidence level of the satellite positioning results can be determined by processing and weighting the parameters. Similarly, the confidence level of laser positioning results can be considered as the reliability of laser positioning. The parameters used in laser positioning can include one or more of the following: number of matching iterations, matching score, and matching quality. The confidence level of the laser positioning results can be determined by processing and weighting the parameters. By comprehensively evaluating both laser and satellite positioning to determine the confidence level of the positioning results, accurate fusion of positioning data can be achieved.

[0027] Optionally, the confidence level of the satellite positioning result is determined based on the parameter information when the object is positioned by satellite, and the confidence level of the laser positioning result is determined based on the parameter information when the object is positioned by laser. This includes: determining the confidence level of the satellite positioning result based on the positioning solution state, number of satellites, and positioning accuracy when the object is positioned by satellite; and determining the confidence level of the laser positioning result based on the number of matching iterations, matching score, and matching quality when the object is positioned by laser.

[0028] The location solution state can include states such as fixed solution, floating-point solution, and no solution. Different parameter values ​​can be set for different location solution states. For example, the parameter values ​​for the location solution state can be represented as follows: The parameter value for the number of satellites can be expressed as: , The current number of effective satellites, The maximum number of satellites for satellite positioning is 25. A higher satellite count indicates better GNSS positioning. Positioning accuracy can be expressed as... , For reference variance, such as setting It is 0.1 meters, that is A value of 0.01 indicates that 10 centimeters represents the maximum permissible variance that the object can tolerate. This parameter represents the position variance output by the satellite positioning system. A higher value for the positioning accuracy parameter indicates better satellite positioning. For example, the confidence level of a satellite positioning result can be expressed as... . Indicates the reliability of satellite positioning. The higher the value, the more reliable the current laser positioning result.

[0029] The parameter value for the number of matching iterations can be expressed as: k represents the actual number of laser matching iterations in the current frame. This represents the maximum allowed number of laser matching iterations, such as 200. A larger value for the number of matching iterations indicates a higher difficulty in algorithm convergence and a lower reliability of the current laser matching. The matching score can be represented as... M is used to count the number of historical frames, for example, if it is 50. This is obtained by matching the laser in the i-th frame. The mean of the laser matching scores for the most recent M frames. The maximum allowable fluctuation value is set to 0.1. A higher matching score indicates less stable laser positioning. The laser matching score can be a direct output of laser positioning; a higher score means a lower match with the map, i.e., the more unreliable the laser positioning. The matching quality parameter is... S is the laser matching score of the current frame. The maximum allowable threshold for the laser matching score is set to 1.5. A higher value for the matching quality parameter indicates a worse laser matching quality. By weighting and fusing the matching iteration count, matching score, and matching quality parameter values, the confidence level of the laser positioning result can be obtained. For example, the confidence level of the laser positioning result is... , This indicates the reliability of laser positioning. Let be the weight coefficient, and satisfy... For example, The confidence levels of the laser positioning results were set to 0.2, 0.2, and 0.6 respectively. The higher the value, the lower the reliability of the current laser positioning result.

[0030] In this embodiment of the invention, a multi-dimensional positioning reliability assessment mechanism is introduced to comprehensively characterize the working status of laser positioning and GNSS positioning. For laser positioning, the reliability is comprehensively evaluated by integrating multi-dimensional indicators that reflect matching convergence characteristics and matching stability, effectively identifying abnormal states such as unstable laser matching and fluctuating results. For GNSS positioning, a comprehensive evaluation is conducted by combining factors such as signal quality and positioning accuracy, avoiding misjudgments caused by relying on a single indicator. By determining the confidence level of the positioning results through multi-dimensional analysis, a more accurate and reliable decision-making basis is provided for the positioning fusion process.

[0031] Step 140: Determine the observation fusion noise based on the confidence level of the satellite positioning results, the confidence level of the laser positioning results, the laser measurement noise, and the satellite measurement noise.

[0032] The confidence level of satellite positioning results can be used to update satellite measurement noise; for example, the confidence level of satellite positioning results is negatively correlated with satellite measurement noise. Similarly, the confidence level of laser positioning results can be used to update laser measurement noise; for example, the confidence level of laser positioning results is positively correlated with laser measurement noise. Furthermore, the updated laser measurement noise and satellite measurement noise are fused to obtain observation fusion noise, which allows for an accurate assessment of the reliability of positioning data, laying the foundation for the reliability and smoothness of positioning fusion. The observation fusion noise from the previous time step can also be considered when determining the observation fusion noise.

[0033] Optionally, the observation fusion noise is determined based on the confidence level of the satellite positioning result, the confidence level of the laser positioning result, the laser measurement noise, and the satellite measurement noise. This includes: adjusting the laser measurement noise based on the confidence level of the laser positioning result and a first adjustment coefficient, and adjusting the satellite measurement noise based on the confidence level of the satellite positioning result and a second adjustment coefficient; determining the current measurement noise from the laser measurement noise and the satellite measurement noise based on the positioning method used by the object at the current moment; and determining the observation fusion noise at the current moment based on the current measurement noise, the observation fusion noise at the previous moment, and the smoothing coefficient.

[0034] For example, through formula Adjust the laser measurement noise using the formula. Adjustments were made to the satellite measurement noise. Among these adjustments, The noise covariance is the fundamental measurement for laser positioning. The basic measurement noise covariance for GNSS positioning; and These are the first and second adjustment coefficients, respectively; for example, both can be set to 5. If the object enters an open area, the matching accuracy decreases. Increase If the object grows rapidly, lidar becomes unreliable. If the object enters indoors, satellite signals are weak. It will shrink rapidly. It will grow larger, at which point satellite positioning becomes unreliable.

[0035] in, and This can be determined experimentally. For example, by continuously collecting 1000 frames of laser positioning and GNSS positioning data at a known precise location, the variance between the measured and true values ​​can be calculated, and the error can be determined based on the standard deviation of the laser positioning position and the standard deviation of the angle. Determined based on the standard deviation of position and angle of satellite positioning For example, after measurement, the standard deviation of laser positioning is approximately 2 cm for position and approximately 1 degree for angle; the standard deviation of GNSS positioning is approximately 5 cm for position and approximately 3 degrees for angle. At this time, , .

[0036] When the object is currently using laser positioning, i.e., when the object receives laser positioning data, the current measurement noise can be determined to be laser measurement noise. When the object is currently using satellite positioning, i.e., when the object receives satellite positioning data, the current measurement noise can be determined to be satellite measurement noise.

[0037] When determining the observation fusion noise at the current moment, this invention introduces a smooth update strategy, such as using... Determine the observation fusion noise at the current moment. For example, if the smoothing coefficient is set to 0.5, For the current measurement noise, This represents the observation fusion noise from the previous moment. Higher observation fusion noise indicates less reliable current positioning data. By continuously adjusting the measurement noise covariance, a smooth transition in positioning modes can be achieved. Introducing a smoothing system avoids abrupt changes in measurement noise, thus ensuring that the object maintains a continuous positioning trajectory without significant jumps in indoor, outdoor, and past indoor / outdoor areas.

[0038] Step 150: Determine the fused pose coordinates of the object at the current moment based on the observation fusion noise, the state covariance matrix of the object localization, the observed pose coordinates, and the predicted pose coordinates.

[0039] The state covariance matrix of object localization represents the uncertainty of object pose estimation, which can be estimated, for example, by calculating the error based on the confidence level of the initial position. When determining the state covariance matrix of object localization, the uncertainty of pose estimation can also be adaptively corrected using observation fusion noise. The observed pose coordinates can be the laser-positioned or satellite-positioned pose of the object in a unified coordinate system currently received. The predicted pose coordinates can be the pose coordinates obtained by predicting the object's motion based on the fused pose coordinates from the previous moment. The fused pose coordinates of the object at the current moment can be obtained by weighted fusion of the observed pose coordinates and the predicted pose coordinates. The weights in the weighted fusion can be determined based on the observation fusion noise and the state covariance matrix of object localization. For example, the weights in the weighted fusion can be positively or negatively correlated with the observation fusion noise; the weights in the weighted fusion can be positively or negatively correlated with the state covariance matrix of object localization.

[0040] By considering the reliability of positioning when determining the fused pose coordinates and by performing smooth positioning data fusion through real-time observation of fusion noise, the fused positioning results can have strong environmental adaptability, adapt to dynamic environmental switching, avoid sudden changes in positioning results when switching between indoor and outdoor scenes, and ensure the continuity and stability of the positioning trajectory.

[0041] Optionally, the fused pose coordinates of the object at the current moment are determined based on the observation fusion noise, the state covariance matrix of the object localization, the observed pose coordinates, and the predicted pose coordinates. This includes: determining the pose observation uncertainty at the previous moment based on the state covariance matrix and the observation fusion noise at the previous moment; updating the state covariance matrix of the object's current localization based on the pose observation uncertainty at the previous moment and the prediction error of the object's kinematic model; determining the Kalman gain based on the state covariance matrix and the observation fusion noise at the current moment; obtaining the predicted pose coordinates of the object at the current moment through the object's kinematic model prediction based on the fused pose coordinates of the object at the previous moment; and determining the fused pose coordinates of the object at the current moment based on the predicted pose coordinates, the Kalman gain, and the observed pose coordinates.

[0042] The pose observation uncertainty at the previous moment can be expressed as: In the formula, k is greater than 1. This represents the state covariance matrix at the previous time step. This is the observation fusion noise from the previous moment. This indicates the degree of confidence in the initial position at the moment the object localization process begins, and can be given by empirical values. For example, When observations are reliable, the observation fusion noise is small, and the pose observation uncertainty is significantly reduced; when observations are unreliable, the observation fusion noise is large, maintaining the observation uncertainty.

[0043] The prediction error of an object's kinematic model can be an inferred error accumulated from the kinematic model over time, or it can be a preset empirical constant matrix. For example, the prediction error of an object's kinematic model... The state covariance matrix at the current positioning moment can be determined by the pose observation uncertainty at the previous moment and the prediction error of the object's kinematic model, such as... This indicates that the observed state at time k is predicted based on the state at time k-1. Adaptive correction of pose observation uncertainty is applied through observation fusion noise, and then the state covariance matrix of the positioning is predicted, which improves the assessment of positioning reliability. Therefore, based on the state covariance matrix and observation fusion noise, the Kalman gain is determined, further enhancing the reliability of positioning fusion.

[0044] The Kalman gain can be positively or negatively correlated with the state covariance matrix; the Kalman gain can also be positively or negatively correlated with the observation fusion noise. For example, the Kalman gain at the current time step is... . It is a weighting coefficient between 0 and 1, when When the value increases, i.e. when the location data is unreliable, This will be reduced accordingly to decrease the weight of the location data.

[0045] The fused pose coordinates of the object at the previous moment are: The kinematic model of an object can be constructed from information such as the direction, velocity, and time of the object's motion. By considering the object's motion based on the fused pose coordinates of the object at the previous moment, the predicted pose coordinates at the current moment can be predicted. . This means predicting the pose coordinates at time k based on the position at time k-1.

[0046] Kalman gain can be used to fuse the predicted pose coordinates and the observed pose coordinates to obtain the fused pose coordinates of the object at the current moment. For example, the fused pose coordinates of the object at the current moment are: .in, Let be the observed pose coordinates of the object at the current moment. The fused pose coordinates of the object at the current moment can be represented as: ,in, This represents the position of the object in a unified coordinate system. Let be the heading angle of the object. By adaptively weighting and fusing the predicted pose and the pose observed by the positioning sensor based on Kalman gain in a unified coordinate system, the current optimal pose estimate of the object, such as a robot, is obtained. Under the condition of dynamic changes in the confidence of laser positioning and GNSS positioning results, multi-source positioning information fusion based on extended Kalman filtering is realized, and the output of continuous, stable and reliable current pose estimate results of the object, such as a robot, in a unified coordinate system is obtained, thus providing a reliable position and attitude basis for the autonomous navigation and motion control of objects such as robots in complex indoor and outdoor environments.

[0047] The technical solution of this embodiment obtains the coordinate system transformation relationship when aligning the coordinate systems of an object during satellite positioning and laser positioning; transforms the current satellite positioning data and laser positioning data of the object to a unified coordinate system based on the coordinate system transformation relationship to obtain the observed pose coordinates of the object at the current moment; determines the confidence level of the satellite positioning result based on the parameter information of the object when using satellite positioning, and determines the confidence level of the laser positioning result based on the parameter information of the object when using laser positioning; determines the observation fusion noise based on the confidence level of the satellite positioning result, the confidence level of the laser positioning result, the laser measurement noise, and the satellite measurement noise; and determines the fused pose coordinates of the object at the current moment based on the observation fusion noise, the state covariance matrix of the object positioning, the observed pose coordinates, and the predicted pose coordinates. This solves the problem of inaccurate positioning during cross-scene positioning. By considering coordinate system unification, the confidence level of the positioning result, and the fusion of measurement errors in the positioning fusion, accurate and continuous positioning of the object is achieved, improving the accuracy of multi-scene positioning. The system ensures smooth positioning during scene transitions. Specifically, a unified coordinate system provides a stable and reliable data foundation for the subsequent fusion of multi-source positioning information, effectively avoiding positioning trajectory breaks or jumps caused by coordinate system incompatibility or alignment errors. By introducing a multi-dimensional positioning sensor reliability assessment mechanism, the working status of laser positioning and GNSS positioning is comprehensively characterized, providing a more accurate and reliable decision-making basis for the fusion process. Based on the reliability assessment results of positioning sensors, the weights of laser positioning and GNSS positioning are dynamically and adaptively adjusted, enabling the fusion process to reasonably allocate the influence of each positioning source according to changes in environmental conditions. In indoor environments, the high-precision advantage of laser positioning is fully utilized, while in outdoor environments, the stability of GNSS positioning is guaranteed. Simultaneously, in indoor-outdoor boundary areas, a smooth transition mechanism is introduced to ensure continuous changes in positioning weights, avoiding abrupt changes in positioning results and thus ensuring the continuity and stability of the positioning trajectory.

[0048] Example 2 Figure 2 This is a flowchart of a fusion positioning method according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method includes: Step 210: Obtain satellite pose data when the object is positioned by satellite and laser pose data when it is positioned by laser, and form a one-to-one target space coordinate pair.

[0049] The satellite pose data of the object at time i is Laser pose data is Satellite pose data and laser pose data at the same moment can be used as target spatial coordinate pairs at that moment. To ensure the accuracy of the coordinate system, the spatial coordinate pairs can be preprocessed. For example, preprocessing may include temporal alignment of the spatial coordinate pairs and / or filtering of the spatial coordinate pairs.

[0050] Optionally, the satellite pose data of the object when using satellite positioning and the laser pose data when using laser positioning are acquired, and a one-to-one corresponding target spatial coordinate pair is formed, including: aligning the satellite pose data and the laser pose data according to the acquisition time to generate a one-to-one corresponding initial spatial coordinate pair; filtering the laser pose data in the initial spatial coordinate pair to determine the target laser pose data that meets the expected positioning; and determining the corresponding target spatial coordinate pair in the initial spatial coordinate pair based on the target laser pose data.

[0051] To ensure the accuracy of spatial correspondence, satellite pose data and laser pose data can be temporally aligned using a nearest neighbor matching method based on timestamps. For example, when At that time, it was assumed that two frames of data could form a set of initial spatial coordinate pairs. Among them, The time when satellite pose data is received. The time it takes to receive laser pose data. Set the preset time threshold, for example, to 10 milliseconds.

[0052] Based on temporal alignment, high-quality initial spatial coordinate pairs can be selected for coordinate system unification. In this invention, the coordinates of satellite pose data can be transformed to the coordinate system used by the laser pose data. Therefore, high-quality laser pose data can be selected for coordinate alignment. Laser pose data can be selected based on laser positioning matching scores or the confidence level of laser positioning results. For example, in the laser positioning matching score... Below the set threshold At that time, that is When selecting target laser pose data, the corresponding laser pose data is used as the target laser pose data (the higher the laser positioning matching score, the worse the actual positioning effect). When selecting and determining target laser pose data, the distance between laser pose data can also be considered; for example, if the adjacent distance is greater than 5 meters, it is used as the target laser pose data. The initial spatial coordinate pairs corresponding to the target laser pose data are used as the target spatial coordinate pairs.

[0053] After the object completes a full-area closed loop around the preset mapping region, a coordinate system alignment trigger signal can be automatically generated to initiate the alignment calculation between the laser positioning and satellite positioning coordinate systems. This triggering mechanism ensures that the calibration sample covers multi-directional feature points within the mapping region, effectively improving the calculation accuracy of the coordinate transformation matrix and avoiding coordinate system alignment deviations caused by uneven spatial distribution of samples.

[0054] Step 220: Establish the coordinate system transformation relationship of the target space coordinate pairs based on the rigid body transformation model.

[0055] The coordinate system transformation relationship includes the rotation matrix and translation vector to be solved. For example, the coordinate system transformation relationship is the transformation relationship when converting from satellite positioning coordinates to laser positioning coordinates. An exemplary coordinate system transformation relationship is... Where R is the rotation matrix and t is the translation vector.

[0056] Step 230: Determine the pose weight of each target spatial coordinate pair based on the matching score of laser positioning in the target spatial coordinate pair.

[0057] A higher matching score in laser positioning results in a lower actual positioning accuracy. Therefore, the matching score in laser positioning can be negatively correlated with the pose weights of the target spatial coordinate pairs. For example, this can be demonstrated by the formula... Determine the pose weights for the target space coordinate pairs. Here, e is the base of the natural logarithm. The pose weights are determined using an exponential decay function for e, ensuring a smooth transition of the weights. The attenuation coefficient controls the rate at which the pose weights decrease as the score increases. For example, setting it to 0.3 can ensure a good matching score. The pose weight of a point with a score of 0.1 is close to 1, while the pose weight of a point with a matching score of 10 is almost 0.

[0058] Step 240: Determine the centroid of the satellite pose data and laser pose data in the target spatial coordinate pair according to the pose weight; and determine the decentralized coordinates of each target spatial coordinate pair according to the centroid.

[0059] Target spatial coordinate alignment data can be divided into satellite pose data and laser pose data. When determining the centroid, the centroid of both the satellite pose data and the laser pose data can be determined. For example, for all laser pose data, the first weighted centroid can be determined as... For all satellite pose data, the second weighted centroid can be determined as... The decentralized coordinates of the target space coordinate pair can include: decentralized coordinates of the laser pose data. Decentralized coordinates of satellite pose data .

[0060] Step 250: Construct the covariance matrix of the target space coordinate pairs based on each decentralized coordinate and its corresponding pose weight; perform singular value decomposition on the covariance matrix to obtain the rotation matrix and translation vector.

[0061] The covariance matrix of the target space coordinate pairs can be ,in, For the i-th point in the decentralized coordinates of the laser pose data, Let be the i-th point in the decentralized coordinates of the satellite pose data. The singular value decomposition of the covariance matrix is... Where U is an m×m orthogonal matrix, and the column vectors are... The eigenvectors of these vectors are called left singular vectors. H is an m×n diagonal matrix with r non-negative elements on the diagonal, called singular values, where r is the rank of H, and the remaining elements are 0; V is an n×n orthogonal matrix with row vectors... The eigenvectors of are called right singular vectors. The rotation matrix is ​​. When the determinant of R, det(R), is less than 0, V is corrected by inverting the last column and then determining the determinant of R to ensure the rotation matrix is ​​valid. The translation vector is... .

[0062] By determining the coordinate system transformation relationship based on the pose weights of the target space coordinate pairs according to singular value decomposition, the accuracy of coordinate system alignment can be guaranteed.

[0063] To further ensure the accuracy of coordinate system alignment, optionally, after performing singular value decomposition on the covariance matrix to obtain the rotation matrix and translation vector, the method further includes: calculating the residual value of each target space coordinate pair based on the rotation matrix and translation vector; removing outlier coordinate points from the target space coordinate pairs based on the residual value and updating the target space coordinate pairs; returning to the step of determining the pose weight of each target space coordinate pair based on the matching score of laser positioning in the target space coordinate pair, until the residual value meets the iteration termination condition to obtain the optimal rotation matrix and translation vector.

[0064] For example, the residual value of the target space coordinate pair can be obtained by formula Confirmed. When the residual value exceeds a preset threshold, such as 0.5 meters, the target spatial coordinate pairs can be removed as outlier points. After removing outlier points, the target spatial coordinate pairs are updated, and the process returns to the step of determining the pose weights of each target spatial coordinate pair based on the matching score of laser positioning. Weighted singular value decomposition is then performed again based on the pose weights, and the rotation matrix and translation vector are updated. The optimal rotation matrix and translation vector can be obtained when the iteration termination condition is met. The iteration termination condition can be residual value convergence, the number of iterations reaching a preset number, or the residual value being less than a preset error value, etc.

[0065] By considering residual values ​​to eliminate outlier pairs when determining coordinate system transformation relationships, we can prevent outliers caused by satellite positioning jumps or laser matching failures from disrupting the coordinate system transformation relationships and ensure the accuracy of coordinate system transformation.

[0066] Step 260: Determine the confidence level of the satellite positioning result based on the parameter information when the object is positioned by satellite, and determine the confidence level of the laser positioning result based on the parameter information when the object is positioned by laser.

[0067] Optionally, the confidence level of the satellite positioning result is determined based on the parameter information when the object is positioned by satellite, and the confidence level of the laser positioning result is determined based on the parameter information when the object is positioned by laser. This includes: determining the confidence level of the satellite positioning result based on the positioning solution state, number of satellites, and positioning accuracy when the object is positioned by satellite; and determining the confidence level of the laser positioning result based on the number of matching iterations, matching score, and matching quality when the object is positioned by laser.

[0068] Step 270: Adjust the laser measurement noise according to the confidence level of the laser positioning result and the first adjustment coefficient, and adjust the satellite measurement noise according to the confidence level of the satellite positioning result and the second adjustment coefficient.

[0069] Step 280: Determine the current measurement noise from the laser measurement noise and the satellite measurement noise based on the positioning method used by the object at the current time.

[0070] Step 290: Determine the current measurement fusion noise based on the current measurement noise, the previous observation fusion noise, and the smoothing coefficient.

[0071] Step 2100: Determine the pose observation uncertainty of the previous moment based on the state covariance matrix of the object at the previous moment and the observation fusion noise of the previous moment.

[0072] Step 2110: Update the state covariance matrix of the object's current position based on the pose observation uncertainty of the previous moment and the prediction error of the object's kinematic model.

[0073] Step 2120: Determine the Kalman gain based on the state covariance matrix at the current time and the observation fusion noise at the current time.

[0074] Step 2130: Based on the fused pose coordinates of the object at the previous moment, the predicted pose coordinates at the current moment are obtained by predicting the object's kinematic model.

[0075] Step 2140: Determine the fused pose coordinates of the object at the current moment based on the predicted pose coordinates, Kalman gain, and observed pose coordinates of the object at the current moment.

[0076] The technical solution of this invention involves acquiring satellite pose data when an object is positioned using satellite positioning and laser pose data when it is positioned using laser positioning, forming a one-to-one corresponding target spatial coordinate pair; establishing the coordinate system transformation relationship of the target spatial coordinate pair based on a rigid body transformation model; determining the pose weight of each target spatial coordinate pair based on the matching score of laser positioning in the target spatial coordinate pair; determining the centroid of the satellite pose data and laser pose data in the target spatial coordinate pair based on the pose weight; determining the decentralized coordinates of each target spatial coordinate pair based on the centroid; constructing the covariance matrix of the target spatial coordinate pair based on each decentralized coordinate and the corresponding pose weight; performing singular value decomposition on the covariance matrix to obtain the rotation matrix and translation vector; determining the confidence level of the satellite positioning result based on the parameter information when the object is positioned using satellite positioning, and determining the confidence level of the laser positioning result based on the parameter information when the object is positioned using laser positioning; adjusting the laser measurement noise based on the confidence level of the laser positioning result and a first adjustment coefficient, and adjusting the satellite measurement noise based on the confidence level of the satellite positioning result and a second adjustment coefficient; and determining the pose weight of each target spatial coordinate pair based on the current position of the object. The positioning method determines the current measurement noise from both laser and satellite measurement noise; it determines the current observation fusion noise based on the current measurement noise, the previous observation fusion noise, and the smoothing coefficient; it determines the pose observation uncertainty at the previous moment based on the state covariance matrix and the previous observation fusion noise; it updates the current state covariance matrix based on the pose observation uncertainty and the object's kinematic model prediction error; it determines the Kalman gain based on the current state covariance matrix and the current observation fusion noise; it predicts the current pose coordinates based on the object's kinematic model using the fused pose coordinates from the previous moment; and it determines the current fused pose coordinates based on the predicted pose coordinates, Kalman gain, and observed pose coordinates. This method solves the problem of inaccurate positioning across scenes by considering coordinate system one, the confidence level of the positioning result, and measurement error fusion in the positioning fusion process, achieving accurate and continuous object positioning and improving the smoothness of positioning during multi-scene switching.

[0077] Specifically, a high-precision alignment method is established between the laser positioning coordinate system and the satellite positioning coordinate system. Based on multiple sets of synchronously acquired satellite positioning data and laser positioning data, a spatial correspondence between the two types of positioning results is constructed. By simultaneously solving the rotation and displacement relationships, accurate alignment of the two coordinate systems under a unified coordinate reference is achieved. This alignment mechanism differs from methods that only perform translation compensation or empirical mapping, providing a stable and reliable coordinate foundation for the fusion of multi-source positioning information. A multi-dimensional confidence assessment of the laser matching results is performed, comprehensively considering the number of matching iterations, the fluctuation characteristics of the matching score, and a pre-set confidence threshold to quantitatively characterize the reliability of laser positioning. This multi-dimensional confidence assessment can effectively identify situations where laser matching surfaces converge but lack stability, or where positioning quality deteriorates due to environmental degradation, significantly outperforming methods that rely solely on a single matching index for validity judgment. Addressing the issue of satellite positioning being susceptible to multipath interference in complex environments such as urban canyons and obstructed areas, a joint confidence assessment mechanism based on multiple indicators, including positioning solution status, the number of available satellites, and positioning accuracy, is constructed. This mechanism accurately reflects the true reliability of satellite positioning under different environmental conditions, avoiding misjudgments caused by relying solely on the number of satellites or signal strength indicators, and providing a reliable basis for fusion decisions. Sensor confidence information is introduced into the extended Kalman filter framework, and the observation covariance matrix is ​​adjusted online based on the real-time confidence levels of laser positioning and satellite positioning, thereby achieving adaptive weight changes for each positioning observation during the fusion process. This adaptive adjustment strategy overcomes the limitations of fixed covariance or static weight fusion methods, enabling the system to enhance its global constraint effect when satellite positioning is reliable and automatically reduce its impact when satellite positioning degrades or fails, significantly improving the stability and robustness of fused positioning and achieving continuous and smooth switching between indoor and outdoor cross-scene positioning modes.

[0078] Figure 3 This is a flowchart of another fusion positioning method provided according to Embodiment 2 of the present invention. Figure 3 As shown, satellite positioning sensors and laser positioning sensors can be installed on the top and front of an object such as a robot. The satellite positioning sensor outputs the robot's three-dimensional pose information in the geodetic coordinate system, i.e., satellite positioning data. The laser positioning sensor outputs the robot's three-dimensional pose information in a local coordinate system, i.e., laser positioning data. Through the established coordinate system transformation relationship, the satellite positioning data can be transformed to the same local coordinate system as the laser positioning data. The confidence levels of the satellite positioning results and the laser positioning results are determined by the parameter information during positioning. When fusing multi-source positioning results, the confidence levels of the satellite positioning results and the laser positioning results are considered and weighted in a unified coordinate system for fusion, ultimately resulting in highly continuous and robust fused pose coordinates.

[0079] Example 3 Figure 4 This is a schematic diagram of a fusion positioning device according to Embodiment 3 of the present invention. Figure 4 As shown, the device includes: a coordinate system transformation relationship acquisition module 410, an observation pose coordinate determination module 420, a positioning result confidence determination module 430, an observation fusion noise determination module 440, and a fusion pose coordinate determination module 450. Wherein: The coordinate system transformation relationship acquisition module 410 is used to acquire the coordinate system transformation relationship of an object when aligning its coordinate system with that of satellite positioning and laser positioning. The observation pose coordinate determination module 420 is used to convert the satellite positioning data and laser positioning data of the object at the current moment to a unified coordinate system according to the coordinate system transformation relationship, so as to obtain the observation pose coordinates of the object at the current moment. The confidence level determination module 430 for positioning results is used to determine the confidence level of satellite positioning results based on the parameter information when the object is positioned by satellite, and to determine the confidence level of laser positioning results based on the parameter information when the object is positioned by laser. The observation fusion noise determination module 440 is used to determine the observation fusion noise based on the confidence level of the satellite positioning results, the confidence level of the laser positioning results, the laser measurement noise, and the satellite measurement noise. The fusion pose coordinate determination module 450 is used to determine the fusion pose coordinates of the object at the current moment based on the observation fusion noise, the state covariance matrix of the object localization, the observed pose coordinates, and the predicted pose coordinates.

[0080] Optionally, the coordinate system transformation relationship acquisition module 410 includes: The target spatial coordinate pair determination unit is used to acquire satellite pose data of the object when using satellite positioning and laser pose data when using laser positioning, and to form a one-to-one corresponding target spatial coordinate pair. The coordinate system transformation relationship establishment unit is used to establish the coordinate system transformation relationship of the target space coordinate pairs based on the rigid body transformation model; the coordinate system transformation relationship includes the rotation matrix and translation vector to be solved; The pose weight determination unit is used to determine the pose weight of each target spatial coordinate pair based on the matching score of laser positioning in the target spatial coordinate pair. The decentralized coordinate determination unit is used to determine the centroid of the satellite pose data and laser pose data in the target spatial coordinate pair according to the pose weight; and to determine the decentralized coordinates of each target spatial coordinate pair according to the centroid. The singular value decomposition unit is used to construct the covariance matrix of the target space coordinate pairs based on each decentralized coordinate and its corresponding pose weight; the singular value decomposition of the covariance matrix yields the rotation matrix and translation vector.

[0081] Optionally, the target spatial coordinate pair determination unit includes: The temporal alignment subunit is used to temporally align satellite pose data and laser pose data according to the acquisition time, and generate a one-to-one corresponding initial spatial coordinate pair. The target laser pose data determination subunit is used to filter the laser pose data in the initial spatial coordinate pair and determine the target laser pose data that meets the expected positioning. The target spatial coordinate pair determination sub-unit is used to determine the corresponding target spatial coordinate pair in the initial spatial coordinate pair based on the target laser pose data.

[0082] Optionally, the coordinate system transformation relationship acquisition module 410 includes: The residual value calculation unit is used to calculate the residual value of each target space coordinate pair based on the rotation matrix and translation vector after the singular value decomposition of the covariance matrix is ​​obtained. The outlier coordinate point removal unit is used to remove outlier coordinate points from the target space coordinate pairs based on the residual values ​​and update the target space coordinate pairs. The iterative execution unit is used to return the steps of determining the pose weights of each target spatial coordinate pair based on the matching score of laser positioning in the target spatial coordinate pair, until the residual value satisfies the iteration termination condition to obtain the optimal rotation matrix and translation vector.

[0083] Optionally, the confidence determination module 430 for the positioning result includes: The confidence level determination unit for satellite positioning results is used to determine the confidence level of satellite positioning results based on the positioning solution status, the number of satellites, and the positioning accuracy during satellite positioning. The confidence determination unit for laser positioning results is used to determine the confidence level of the laser positioning results based on the number of matching iterations, matching score, and matching quality during laser positioning.

[0084] Optionally, the observation fusion noise determination module 440 includes: The measurement noise adjustment unit is used to adjust the laser measurement noise according to the confidence level of the laser positioning result and a first adjustment coefficient, and to adjust the satellite measurement noise according to the confidence level of the satellite positioning result and a second adjustment coefficient. The current measurement noise determination unit is used to determine the current measurement noise from laser measurement noise and satellite measurement noise based on the positioning method used by the object at the current moment. The observation fusion noise determination unit is used to determine the observation fusion noise at the current moment based on the current measurement noise, the observation fusion noise at the previous moment, and the smoothing coefficient.

[0085] Optionally, the pose coordinate determination module 450 includes: The pose observation uncertainty determination unit is used to determine the pose observation uncertainty of the previous moment based on the state covariance matrix of the object at the previous moment and the observation fusion noise at the previous moment. The state covariance matrix update unit is used to update the state covariance matrix of the object's current positioning based on the pose observation uncertainty of the previous time step and the prediction error of the object's kinematic model. The Kalman gain determination unit is used to determine the Kalman gain based on the current state covariance matrix and the current observation fusion noise. The predicted pose coordinate determination unit is used to predict the current pose coordinates based on the fused pose coordinates of the object at the previous moment and through the object's kinematic model. The fusion pose coordinate determination unit is used to determine the fusion pose coordinates of the object at the current moment based on the predicted pose coordinates, Kalman gain, and observed pose coordinates of the object at the current moment.

[0086] The fusion positioning device provided in the embodiments of the present invention can execute the fusion positioning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0087] Example 4 Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0088] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) or random access memory (RAM), communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. Input / output (I / O) interfaces are also connected to the bus 14.

[0089] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0090] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as fusion localization methods.

[0091] In some embodiments, the fusion positioning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the fusion positioning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the fusion positioning method by any other suitable means (e.g., by means of firmware).

[0092] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0095] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0096] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0097] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0098] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0099] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A fusion positioning method, characterized in that, include: Obtain the coordinate system transformation relationship when aligning an object's coordinate system using satellite positioning and laser positioning; Based on the coordinate system transformation relationship, the satellite positioning data and laser positioning data of the object at the current moment are transformed into a unified coordinate system to obtain the observed pose coordinates of the object at the current moment; The confidence level of the satellite positioning result is determined based on the parameter information of the object when satellite positioning is used, and the confidence level of the laser positioning result is determined based on the parameter information of the object when laser positioning is used. The observation fusion noise is determined based on the confidence level of the satellite positioning results, the confidence level of the laser positioning results, the laser measurement noise, and the satellite measurement noise. The fused pose coordinates of the object at the current moment are determined based on the observed fusion noise, the state covariance matrix of the object localization, the observed pose coordinates, and the predicted pose coordinates.

2. The method according to claim 1, characterized in that, Obtain the coordinate system transformation relationship when aligning an object's coordinate system using satellite positioning and laser positioning, including: Acquire satellite pose data of an object when using satellite positioning and laser pose data when using laser positioning, and form a one-to-one target space coordinate pair; The coordinate system transformation relationship of the target space coordinate pairs is established based on the rigid body transformation model; the coordinate system transformation relationship includes the rotation matrix and translation vector to be solved. The pose weight of each target spatial coordinate pair is determined based on the matching score of laser positioning in the target spatial coordinate pair; The centroids of the satellite pose data and laser pose data in the target spatial coordinate pair are determined based on the pose weights; and the decentralized coordinates of each target spatial coordinate pair are determined based on the centroids. The covariance matrix of the target space coordinate pairs is constructed based on each decentralized coordinate and its corresponding pose weight; the singular value decomposition of the covariance matrix is ​​performed to obtain the rotation matrix and translation vector.

3. The method according to claim 2, characterized in that, Acquire satellite pose data for objects when using satellite positioning and laser pose data when using laser positioning, and form one-to-one corresponding target spatial coordinate pairs, including: The satellite pose data and the laser pose data are time-aligned according to the acquisition time to generate a one-to-one corresponding initial spatial coordinate pair. The laser pose data in the initial spatial coordinate pair is filtered to determine the target laser pose data that matches the expected positioning. Based on the target laser pose data, the corresponding target spatial coordinate pair is determined in the initial spatial coordinate pair.

4. The method according to claim 2, characterized in that, After performing singular value decomposition on the covariance matrix to obtain the rotation matrix and translation vector, the method further includes: Calculate the residual value of each target space coordinate pair based on the rotation matrix and the translation vector; Based on the residual value, abnormal coordinate points are removed from the target spatial coordinate pair, and the target spatial coordinate pair is updated. Return to the step of determining the pose weight of each target spatial coordinate pair based on the matching score of laser positioning in the target spatial coordinate pair, until the residual value satisfies the iteration termination condition to obtain the optimal rotation matrix and translation vector.

5. The method according to claim 1, characterized in that, The confidence level of satellite positioning results is determined based on the parameter information of the object when using satellite positioning, and the confidence level of laser positioning results is determined based on the parameter information of the object when using laser positioning, including: The confidence level of the satellite positioning result is determined based on the positioning solution status, the number of satellites, and the positioning accuracy during satellite positioning. The confidence level of the laser positioning result is determined based on the number of matching iterations, matching score, and matching quality during laser positioning.

6. The method according to claim 1, characterized in that, Based on the confidence level of the satellite positioning results, the confidence level of the laser positioning results, the laser measurement noise, and the satellite measurement noise, the observation fusion noise is determined, including: The laser measurement noise is adjusted based on the confidence level of the laser positioning result and a first adjustment coefficient, and the satellite measurement noise is adjusted based on the confidence level of the satellite positioning result and a second adjustment coefficient. The current measurement noise is determined from the laser measurement noise and the satellite measurement noise based on the positioning method used by the object at the current moment; The observation fusion noise at the current moment is determined based on the current measurement noise, the observation fusion noise at the previous moment, and the smoothing coefficient.

7. The method according to claim 1, characterized in that, Based on the observed fusion noise, the state covariance matrix of the object localization, the observed pose coordinates, and the predicted pose coordinates, the fused pose coordinates of the object at the current moment are determined, including: Based on the state covariance matrix and the observation fusion noise of the previous time step when the object was located, the pose observation uncertainty of the previous time step is determined. Based on the pose observation uncertainty of the previous moment and the prediction error of the object's kinematic model, update the state covariance matrix of the object's current positioning. The Kalman gain is determined based on the state covariance matrix at the current time and the observation fusion noise at the current time. Based on the fused pose coordinates of the object at the previous moment, the predicted pose coordinates at the current moment are obtained by predicting the object's kinematic model. The fused pose coordinates of the object at the current moment are determined based on the predicted pose coordinates of the object at the current moment, the Kalman gain, and the observed pose coordinates.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the fusion positioning method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the fusion positioning method according to any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the fusion positioning method according to any one of claims 1-7.

Images