65results about How to "Improve pose estimation accuracy" patented technology

A method and system detects and localizes an object by first acquiring a frame of a three-dimensional (3D) scene with a sensor, and extracting features from the frame. The frame are segmented into segments, wherein each segment includes one or more features, and for each segment, searching an object map for a similar segment, and only if there is a similar segment in the object map, registering the segment in the frame with the similar segment to obtain a predicted pose of the object. The predicted poses are combined to obtain the pose of the object, which can be outputted.

The invention discloses a spacecraft attitude estimation method based on norm-constrained cubature Kalman filter. The method comprises the following steps: a state equation of a spacecraft navigation system is established; a non-linear measurement equation of the spacecraft navigation system is established; a discretized state equation and a discretized measurement equation are obtained; a norm-constrained cubature Kalman filtering algorithm is used for the discretized state equation and the discretized measurement equation, and an attitude of the spacecraft is outputted. The norm-constrained cubature Kalman filter is used, norm constraint of quaternion for describing the spacecraft attitude is introduced into the attitude estimation, so that the problem of singular covariance existed in the process of attitude estimation without considering norm constraint is effectively solved, the influences of norm constraint conditions of quaternion on the spacecraft attitude estimation are effectively corrected by only adding small computational complexity, precision of attitude estimation is improved, navigation precision of the spacecraft is furthermore improved, and stability of the navigation process is enhanced.

The invention discloses a workpiece pose estimation method based on point cloud, which is mainly used for solving the problem of workpiece pose estimation in the field of automatic assembly, especially in the field of small part assembly. The method comprises the steps of point cloud data preprocessing, point cloud virtual view extraction and pose estimation. Wherein the pose estimation step further comprises the steps of performing iterative operation by adopting a particle filter algorithm based on a dynamic model of an iterative closest point algorithm, outputting the pose of the effectiveparticle in a weighted mean mode if an iterative stop condition is met, and calculating the pose of the target workpiece relative to the camera coordinate system through inversion operation. The invention also relates to an apparatus comprising a memory and a processor implementing the above method steps when the processor executes a program stored in the memory.

The invention discloses a vector constraint-based random characteristic point selection method for landing position detection. The method comprises the steps of 1, performing acquisition and synchronous uploading of a landing region image; and 2, processing the landing region image: 201, processing the landing region image at an initial sampling moment; 202, processing the landing region image at the next sampling moment by Harris corner extraction, Harris corner matching, SIFT characteristic extraction, SIFT characteristic point matching, characteristic point fusion, characteristic point combination generation and optimal characteristic point combination screening; and 203, returning to the step 202 of processing the landing region image at the next sampling moment. The method is simple in step, reasonable in design, convenient to realize, high in practicality and good in using effect; characteristic points for pose estimation can be simply, conveniently and quickly selected from characteristic points extracted from unknown landing region images; and the pose estimation precision can be effectively improved.

The present invention discloses a combination navigation method, which comprises: acquiring an attitude measurement value in a first speed interval range by using a first combination filtering, wherein the first combination filtering is the INS/GNSS combination filtering with an observation equation containing the INS and GNSS attitude angle measurement difference; acquiring an attitude measurement value in a second speed interval range by using a second combination filtering and an added attitude filtering, wherein the second combination filtering is the INS/GNSS combination filtering being not contain the INS and GNSS attitude angle measurement difference, and the added attitude filtering is used for carrying out attitude filtering on the filtering result of the second combination filtering; and acquiring an attitude measurement value in a third speed interval range by using the second combination filtering. The invention further discloses a combination navigation system. With the method and the system of the present invention, the attitude observability and the estimation precision are improved.

The invention discloses a method for determining a relative attitude of a microsatellite by using a dual array antenna. A relative attitude dynamics equation is used as a system equation to form a probe frame, and then a transmitting and receiving timing sequence is designed; two array antennas of a main spacecraft receive a transmitted signal from a slave spacecraft array antenna which is used asan output signal, and moving correlation is carried out according to the output signal and a probe signal, so as to obtain an array impulse response matrix; two-dimensional spectral peak search is performed to obtain two sets of wave leaving angles and wave arrival angles; four unit line-of-sight vectors are obtained by using the two sets of wave leaving angles and wave arrival angles; a measurement equation is constructed by using the bidirectional unit line-of-sight vectors and a relative attitude rotation matrix to obtain a measurement noise variance matrix; the TRIAD algorithm is used toobtain an attitude quaternion at the initial moment; and then the relative attitude and a relative rotational angular velocity which are convergent gradually are obtained according to the system equation and the measurement equation. The method for determining the relative attitude of the microsatellite by using the dual array antenna has low computational complexity, high estimation precision andwide application range.

The invention discloses an adaptive combination navigation antenna beam control method for a satellite communication in motion communication system. The method comprises the following steps: (1) building a system equation of the satellite communication in motion system, calculating the state variable x of a combination navigation attitude estimation system according to the position error amount deltar of a carrier, the speed error amount deltav of the carrier and the attitude angle error amount Psi of the carrier, and meanwhile setting the observed quantity of a satellite communication in motion combination navigation attitude estimation algorithm with a main controller; (2) building a corresponding satellite communication in motion combination navigation measurement equation according to different driving environments of the carrier, and solving a measurement equation coefficient matrix Hk; and (3) performing attitude computation through adaptive extended Kalman filter to obtain the three-dimensional attitude angle of the carrier, and controlling three motors to adjust the direction of antenna beams according to the three-dimensional attitude angle of the carrier in order that an antenna is aligned with a satellite. Through implementation of the method, accurate alignment between the antenna and the satellite can be realized, and the cost is low.

The invention discloses a method for estimating attitude and parameters of a non-cooperative spacecraft without a gyroscope, and the method comprises the following steps of considering the problem that the relative coordinate system of the target non-cooperative spacecraft established according to the optical sensor does not coincide with its body coordinate system, establishing an attitude kinematics and dynamics model of the non-cooperative spacecraft of the quick-roll space in the relative coordinate system of the target non-cooperative spacecraft; selecting the error of the attitude quaternion vector part, the three-axis rotational angular velocity and the rotary inertia ratio of the target non-cooperative spacecraft as the filtering variable, and deducing the discrete state equationsand observation equations of the linearized system; designing a multiplicative extended Kalman filter, and improving the five-step prediction to improve the accuracy of filtering estimation and the fastness of follow-up, thereby solving the problem of increased filtering errors caused by over-low sampling frequency of the optical sensor.

The invention discloses a spatial non-cooperative target pose estimation method based on deep learning, and the method comprises the steps: 1, dividing pose category intervals, generating spatial non-cooperative target image data, and marking a pose category label, a pose numerical value label and a position label, and obtaining a marking data set of a spatial non-cooperative target, which comprises a training set, a test set and a verification set; 2, constructing a neural network applied to spatial non-cooperative target pose estimation based on an AlexNet network, removing full connection layers at the tail end of the network, and then connecting the four full connection layers in parallel; 3, designing loss functions of four branches; 4, inputting the training set and the verification set into the constructed neural network, training the network by utilizing a designed loss function, and storing a neural network model when the loss function is converged to a global minimum value; and 5, performing pose estimation on the spatial non-cooperative target by using the trained neural network model. According to the invention, pose estimation of a spatial non-cooperative target can be realized through a single camera and a single image.

The invention discloses a pose calculation method combining deep learning and geometric optimization. The system comprises a pose estimation module PoseNet and a depth estimation module DepthNet, a pose estimation method based on deep learning and an optimization strategy based on geometric constraints are integrated into a visual odometer frame, luminosity errors, feature point reprojection errors, adjacent inter-frame constraints and constraints in a local map formed by a section of continuous frames are calculated, and real-time and accurate pose estimation can be carried out.

An embodiment of the invention discloses a method for determining vehicle pose, a vehicle-mounted system and a vehicle, which obtain a fused pose of the vehicle according to pose output fusion of monocular systems, thereby improving the pose estimation precision of the vehicle. The method of the embodiment of the invention comprises the steps of: acquiring first inertial navigation information andfirst vehicle odometer information of the vehicle; determining a first current estimated pose of the vehicle according to the first inertial navigation information and the first vehicle odometer information; acquiring N monocular camera external parameters of the vehicle, wherein N is an integer greater than 1; integrating an inertial navigation mileage calculation method and a visual SLAM algorithm of each monocular camera according to the N monocular camera external parameters and the first current estimated pose, so as to estimate current poses of the N monocular cameras; performing coordinate transformation on the current poses of the N monocular cameras respectively to obtain N converted poses of the vehicle; and acquiring the fused pose of the vehicle according to the N converted poses.

The invention belongs to the field of underwater geomagnetic assisted navigation, and particularly relates to an attitude estimation method and device for a multiplicative error quaternion based geomagnetic tensor field assisted gyroscope. A state equation of the attitude estimation device is established by error quaternion, gyroscope scale factor error and gyroscope scope constant bias error, andvector magnetometer groups in strapdown to a carrier are used for measuring five independent components of a geomagnetic tensor field under the carrier system; measurement values of the five independent components are subjected to subtraction with measured values of the independent components of the geomagnetic tensor field under the carrier system, and the independent component difference of thegeomagnetic tensor field is obtained; a Kalman filter is used for estimation and compensation, and estimated values of the attitude quaternion, gyroscope scale factor and constant bias are obtained;attitude of the carrier is determined by the attitude quaternion. The problem of singularity of the state covariance matrix is solved by using multiplicative error quaternion as the attitude estimation variable; the influence of vector direction of the geomagnetic background field on attitude estimator system observation is reduced by using the difference of the independent components of the geomagnetic tensor field as observed quantity.

The invention provides a pan-tilt attitude estimation method based on extended Kalman filtering. The method employs quaternion to represent the current attitude of an object, a system state quantity comprises the offset error of quaternion and angle increment, an accelerometer and a magnetometer are used to correct the offset error of angle increment, and the attitude estimation is be more accurate. Accelerometer correction and magnetometer correction are implemented in two stages so that the accelerometer correction and the magnetometer correction do not interfere with each other, and the attitude estimation accuracy is improved; and in the correction of the accelerometer, the third vector of the quaternion in the correction is set to be zero, and in the correction of the magnetometer, the first vector and the second vector of the quaternion in the correction are set to be zero. According to the invention, more accurate attitude estimation information can be obtained.

The invention discloses a measurement delay filtering method for a small field star sensor, and belongs to the technical field of aircraft attitude estimation. In view of the conditions that multiplicative noise exists in a non-linear attitude estimation system state model and unknown interference exists in a measurement model, an aircraft attitude estimation model with the existence of the indetermination of the two models is established, meanwhile, the upper boundary ranges of prediction variance and estimation variance are found by using a robust algorithm design based on an expansion Kalman filtering structure, and then optimal filtering gain is designed by using a minimum variance theoretical design. The aircraft attitude is determined by using expansion Kalman filtering, the truth isminimum variance criterion estimation which is different from that of general frequency domain filters, the method can be used for processing attitude estimation under undetermined conditions, the method has the advantages of high instantaneity, and low dependence degree on prior information, the problem that delay exists in the star sensor is solved, and relatively high attitude estimation accuracy can also be guaranteed even if in information uncertainty conditions.

The invention relates to a small unmanned aerial vehicle MARG heading and attitude estimation method based on an adaptive EKF algorithm, and belongs to the technical field of integrated navigation. According to the method, firstly, measurement updating of a magnetometer and an acceleration meter are carried out separately, magnetometer error evaluation is added, when the magnetometer error is large, the magnetometer error can be isolated, and interference on horizontal attitude estimation is avoided; secondly, the invention further provides an adaptive filtering algorithm for the external acceleration, in order to improve the estimation precision, a triaxial acceleration module value is not directly judged, a triaxial residual error is analyzed, and then corresponding measurement noise issubjected to self-adaptive adjustment, so that useful acceleration information is prevented from being lost, and the attitude estimation precision is improved.

The invention provides a dynamic residual threshold adaptive quaternion particle filter attitude calculation data fusion method, which belongs to the technical field of digital filtering and multi-sensor data fusion, and is mainly used for improving the precision of carrier attitude estimation. According to the method, a standard particle filter algorithm is used as a framework, data of a gyroscope, an accelerometer and a magnetometer are fused, a relatively accurate sensor measurement model is established by analyzing sensor errors, quaternion is selected as an attitude parameter, a reasonable range of residual errors is predicted and judged, then a measurement noise matrix of a system is adaptively adjusted, and data fusion of sensors is realized. The method is suitable for a nonlinear attitude measurement system, has good anti-interference performance and attitude calculation precision, and can effectively solve the problem of carrier attitude calculation in a complex environment.

The invention discloses a visual inertial integrated navigation method fusing semantic features. The method comprises steps of RGBD visual sensor data S(k), accelerometer data and gyroscope data at the k moment being collected, and the current pose T (k) of a camera being obtained through calculation by using a visual odometer according to the visual sensor data S (k); performing semantic plane feature extraction and matching between two adjacent image frames by using the visual sensor data S (k); performing pre-integration between two adjacent image frames by using inertial sensor data; combining the semantic plane observation residual error, the visual odometer relative pose observation residual error and the inertia pre-integration residual error to optimize and solve the carrier navigation information; and outputting the carrier navigation information and camera internal parameters. According to the method, positioning accuracy and robustness of the navigation system can be effectively improved.

The invention relates to an iterative 6D pose estimation method and device based on deep learning, and the method comprises the steps: carrying out the coding of the output of a 3D-2D key point corresponding relation extraction network, and coding the output of the 3D-2D key point corresponding relation extraction network into an input format of a pose coarse estimation network; the method comprises the following steps that: firstly, object 6D postures are input into a pose coarse estimation network constructed by utilizing an MLP, a pooling layer and a full connection layer, so that the pose coarse estimation network is combined into an integral network, the object 6D postures can be directly output, an end-to-end 6D object pose estimation network is formed, and a loss function of the 6D object pose estimation network is a function capable of directly reflecting and resolving object 6D pose parameters; and optimizing 6D attitude parameters output by the 6D object pose estimation network by using an orthogonal iterative algorithm. According to the method, the problems of long time consumption and poor repeatability of calculating the target attitude based on the PnP algorithm when many abnormal values exist are solved, and the attitude estimation efficiency and the attitude estimation robustness and accuracy are improved.