404results about How to "Eliminate cumulative errors" patented technology

The invention belongs to the crossing field of computer vision and intelligent robot, and discloses a new multi-feature multi-sensor method for a mobile robot to track a moving body. The method comprises the following steps: 1, coarsely positioning a body carrying a passive tag around a radio frequency identification (RFID) system by using the RFID system; 2, initially positioning the body in an image by using an adaptive template matching algorithm based on head and shoulder features; 3, accurately positioning the body in the image by using a multi-feature-based mean-shift algorithm; 4, predicting the moving state of the body by using a extended Kalman filter algorithm; 5, screening the acquired target position information by using a double-layer collaboration positioning mechanism; and 6, controlling the robot to move along with the body by using a robot following control algorithm. By the method, bodies with different poses can be tracked, the problem that the tracking is influenced when a target suddenly turns and is shielded is solved, and the robot can accurately, stably and continuously track the moving body.

The invention relates to a visual inertial navigation SLAM method based on ground plane hypothesis. According to the method, feature points are extracted from an image to perform IMU pre-integration,a camera projection model is established, and camera internal parameter calibration and external parameter calibration between an IMU and a camera are performed; a system is initialized, a visually observed point cloud and a camera pose are aligned to the IMU pre-integration, and a ground equation and the camera pose are restored; the ground is initialized to obtain a ground equation, the ground equation under the current camera pose is determined and back projected to an image coordinate system, and a more accurate ground region is acquired; and based on state estimation, all sensor observation models are derived, camera observation, IMU observation and ground feature observation are fused to do state estimation, a graph optimization model is used to do state estimation, and a sparse graph optimization and gradient descent method is used to realize overall optimization. Compared with previous algorithms, the precision of the method is greatly improved, estimation of the camera pose can be limited globally, and therefore accuracy is greatly improved.

The invention relates to a method for estimating the SOC (state of charge) of equalized batteries. The method includes steps of estimating the SOC of the batteries before battery equalization by ampere-hour integration; creating an equalized battery equivalent model and an equivalent circuit; combining an ampere-hour method on the basis of the equalized battery model, and creating a corresponding mathematical model; discretizing the mathematical model to obtain a state-space equation; and estimating the state of charge of voltage at a battery end by an extended Kalman filter algorithm, and finally obtaining an estimation value of the SOC of the equalized battery. By the aid of the method, the SOC of equalized current and a battery or a battery pack can be accurately estimated when a battery management system starts equalization, accumulated battery SOC estimation errors of the ampere-hour method can be eliminated, and noise further can be suppressed.

The invention discloses a wearable personnel gait-detection indoor-positioning system and a method therefor. The wearable personnel gait-detection indoor-positioning system comprises attitude sensors, a data acquisition device, a data transmission unit and a data processing unit. The attitude sensors are installed respectively at leg parts and the waist part of a user. A first input end of the data acquisition device is connected to the attitude sensors. A second input end of the data acquisition device is connected to a correction module. The data transmission unit and the data processing unit are orderly connected to an output end of the data acquisition device. The data acquisition device acquires data measured by the attitude sensors. The data transmission unit transmits the acquired data to the data processing unit. The data processing unit carries out data synthesis processing to obtain attitude angles of the corresponding parts, carries out gait detection according to the attitude angles, acquires walk step length and a walk direction according to leg and waist length parameters of the user, and carries out vector superposition of the walk step length and the walk direction to obtain user positions in the room. The wearable personnel gait-detection indoor-positioning system and the method can simultaneously realize gait detection and positioning and has high positioning precision.

The invention discloses a lithium battery SOC estimation method of off-line data segmentation correction. The method is mainly applied to a battery management system of an electric vehicle and is used for correcting an actual capacity of a lithium battery and eliminating an accumulation error of a traditional ampere-hour integral method. The method comprises the following steps of establishing a battery equivalence circuit model; acquiring an OCV-SOC curve; using a terminal voltage response curve at the time that battery discharging is ended to carry out off-line parameter identification on the equivalence circuit model; calculating a battery health state SOH; using an ampere-hour integral method to calculate a current value of the SOC in real time; using the battery health state to correct a SOC value; using off-line data to carry out segmentation elimination on the accumulation error in the ampere-hour integral method. By using the method of the invention, the battery SOC can be accurately estimated and the accumulation error of battery SOC estimation performed through using the ampere-hour integral method is eliminated.

The invention discloses a battery capacity correction method based on an improved ampere-hour integral method. The method comprises the following steps: enabling a battery pack to run in a vehicle-mounted way; calculating the state of charge (SOC) by the ampere-hour integral method; judging whether the electric quantity of batteries is too low or not; judging whether the minimum battery voltage V<min> is lower than the threshold voltage V<mdy> in a capacity correction mode or not; entering the capacity correction mode; carrying out capacity correction, and then quitting the capacity correction mode; charging the batteries, and calculating the SOC; judging whether the maximum voltage V<max> of the battery pack is higher than charge cut-off voltage V<h> or not; finishing charging, storing the battery capacity Q accumulated in the charging process, and replacing the previous battery capacity Q<n> by the battery capacity Q; modifying the current battery capacity value to be Q, and modifying the value of the SOC. According to the method, the SOC values of the batteries and the stored electric quantity values are corrected at the end of the battery charging process, so that the accurate initial value of the SOC can be obtained after every time of charging is finished; furthermore, at the end of discharge, the batteries can select to enter the battery capacity correction mode, so that the current capacities of the batteries can be corrected, and thus the calculation error caused by battery aging can be eliminated.

The invention comprises an intelligent warehousing mobile robot system. The intelligent warehousing mobile robot system comprises a mobile phone client, a robot module and a server, wherein the mobile client, the server and the robot module communicate with one another through a wireless communication network; the robot module comprises an ARM chip, an arduino chip, a camera, a motor driving module and a robot body; the server carries out path planning through adopting an A*path planning algorithm so as to obtain paths only containing inflection point coordinates and sends the paths to the robot module, so that control and direction correction of the robot module can be realized; the robot module carries out internal positioning through using a gyroscope and a mileage meter, and at the same time, the camera scans a two-dimensional code on the ground of a warehouse, so that the external positioning of the robot module can be carried out through using the arrangement scheme of the two-dimensional code of the warehouse, and therefore, the correction of the internal positioning can be realized. With the intelligent warehousing mobile robot system of the invention adopted, the accumulated error of the internal positioning can be effectively eliminated, accurate positioning can be obtained, and therefore, the robot can reach a destination more accurately to prepare for cargo transport.

The invention provides an SOC online measurement method for a lithium battery based on the Gaussian mixture process and the dynamic OCV correction. According to the invention, the Gaussian mixture regression (GMR) process is integrated with a Gaussian mixture model and a Gaussian process regression model, so that the time series of dynamic non-linearity can be effectively represented. The dynamic OCV correction method can calibrate an OCV-SOC curve according to external factors, so that the accurate OCV is obtained. Therefore, the SOC is corrected, and the accumulative error is eliminated. In this way, a battery model can be updated in real time according to the appropriate algorithm difficulty under the complex working condition of an automobile. Meanwhile, battery characteristics can be accurately tracked and the accumulated estimation error is corrected. The long-term precision is guaranteed.

The invention provides a method for acquiring movement attitude data. The method comprises the following steps of: recording the geomagnetic field intensity of a moving object at a first measurement course angle as first magnetic field intensity; when the moving object moves to a second measurement course angle, rotating the direction of the geomagnetic field intensity of the position where the moving object is at an angle of difference of the second measurement course angle and the first measurement course angle, and recording the geomagnetic field intensity of the position as a second magnetic field vector; calculating the first magnetic field intensity and the second magnetic field intensity according to a preset rule to obtain magnetic field intensity observation quantity; and performing recursion autoregression filter on the magnetic field intensity observation quantity and measurement attitude data of the moving object to obtain the attitude data of the moving object. The invention also provides a device for acquiring movement attitude data and a method and a system for tracking the human movement attitude. The geomagnetic field intensity is introduced into the method, so that the problem of error accumulation produced during measurement of angular velocity is solved.

The invention discloses a multimodal fusion based indoor self-positioning method. The method includes the following steps of (1) user track deduction and (2) user track calibration. The invention further discloses a multimodal fusion based indoor self-positioning device. The multimodal fusion based indoor self-positioning method and device has the advantages that on the basis of geomagnetism track matching and indoor feature points, track deduction results are calibrated, accumulated errors are eliminated, and indoor self-positioning precision is improved effectively. Therefore, accurate positioning and navigation of users can be realized under the condition of no dependence on external environment deployment and no need of preliminary training or in case of emergency.

The invention relates to an unmanned aerial vehicle scene dense reconstruction method based on VISLAM and depth estimation, and the method comprises the steps: (1) fixing an inertial navigation unit IMU to an unmanned aerial vehicle, and calibrating the internal parameters and external parameters of a monocular camera of the unmanned aerial vehicle and the external parameters of the IMU; (2) collecting an image sequence and IMU information of an unmanned aerial vehicle scene by using an unmanned aerial vehicle monocular camera and an IMU; (3) processing the image and the IMU information acquired in the step (2) by using VISLAM to obtain a camera pose with scale information; (4) inputting the monocular image information as an original view into a viewpoint generation network to obtain a right view, and inputting the original view and the right view into a depth estimation network to obtain depth information of the image; (5) combining the camera attitude obtained in the step (3) with the depth map obtained in the step (4) to obtain a local point cloud; and (6) through point cloud optimization and registration, fusing the SLAM tracking trajectory with the local point cloud to obtainan unmanned aerial vehicle scene dense point cloud model.

The invention discloses an inertial navigation-laser scanning integrated coal mining machine positioning device and method, and belongs to coal mining machine positioning devices and methods. The positioning device adopts the structure that a positioning device explosion-proof housing and a laser signal receiving module are fixed on the main body of a coal mining machine; an inertial navigation positioning device and a laser scanning microprocessor are mounted in an explosion-proof device; when the coal mining machine works, the inertial navigation positioning device acquires the real-time angular rate and the real-time through a sensor, and transmitting the data to the laser scanning microprocessor; in a laser scanning device, a laser scanning base station is arranged in the working area of the coal mining machine, a laser signal of the laser scanning base station is received by the laser signal receiving module, and meanwhile the data are transmitted to the laser scanning microprocessor; the laser scanning microprocessor is connected with an upper computer through a serial port. Respectively acquired positioning data are transmitted to a coal mining machine positioning control system for data processing, and the position of the coal mining machine is confirmed according to an algorithm integrating the least square method and the neural network algorithm, so as to realize precise positioning; the positioning device and method have the advantages of being precise in positioning, safe and reliable.

The invention relates to an automatic solar tracking system which tracks the sun accurately in real time and is used for efficiently collecting and using solar energy. The automatic solar tracking device comprises a solar energy photovoltaic array, an adjusting device connected with the solar energy photovoltaic array and used for adjusting the position of the solar energy photovoltaic array, a stand column for supporting the adjusting device and a control system. The adjusting device is composed of a pitch angle adjusting assembly and a position angle adjusting assembly, wherein the pitch angle adjusting assembly is connected with the position angle adjusting assembly through a vertical shaft, the position angle adjusting assembly is arranged on the stand column, and the control system is used for performing closed loop control on the adjusting device. The accuracy in solar tracking of the automatic solar tracking device is smaller than 0.1, the energy consumption only occupies for 0.5% of the electric energy production of the driving photovoltaic array, and the automatic solar tracking system has the advantages of being long in service life, intact in protection function and the like.

The invention is applicable to the field of industrial control and provides a sampling clock synchronizing method. The method comprises the following steps of: receiving crystal oscillator pulse signals provided by an external crystal oscillator and generating count pulse signals; receiving the count pulse signals for counting, and outputting sampling pulse signals after the counting of a preset counting quantity N is finished; receiving externally-input time pulse signals, and counting errors of the count pulse signals within a clock pulse signal period and between the clock pulse signal period and a sampling pulse signal period; and changing the preset counting quantity N according to the errors. By adopting the external stable clock pulse signals, the sampling clock synchronizing method can be used for adjusting the inherent errors of the crystal oscillator pulse singles and the errors caused by the factor that the count pulse signals trigger the sampling pulse signals under the influence of temperature change of work environments, so that the errors of crystal oscillator frequency can be reduced to the greatest extent, the effect of the accumulated errors on a sampling pulse can be eliminated, and the error accumulation of a sampling pulse clock is overcome.

The invention provides a tracking method of a relative movement angle and an angular velocity of a slowly rotating space fragment. The method comprises the following steps: after image information of a slowly rotating space fragment object is acquired, performing extraction, matching, tracking and pose solving of features points on current frames; performing object local space point three-dimensional reconstruction; performing object global closed-loop detection to eliminate accumulative errors; and obtaining an angular velocity and a total angle of object movement, and eliminating interframe revolution errors. According to the invention, from the perspective of image features, by use of a graph optimization method, tracking measurement of the slowly rotating space fragment is realized through a feature relay between key frames and key points, at the same time, the accumulative errors are eliminated by use of a closed-loop detection means, and thus the measurement precision is improved.

The invention belongs to the technical field of unmanned aerial vehicle navigation indoor positioning, and discloses an unmanned aerial vehicle vision-inertia fusion indoor positioning method. The method comprises steps of obtaining scale information of the feature points by using a binocular parallax method; tracking the feature points by using a pyramid LK optical flow algorithm; removing the feature points which are mismatched; pre-integrating inertial measurement data, and keeping data synchronization between sensors; and constructing a sliding window, carrying out selection and marginalization management on the key frame, and estimating the position, attitude, speed and gravity direction of the unmanned aerial vehicle and the drift of an inertial device by adopting a graph optimization mode. Finally, the functions of indoor positioning of the unmanned aerial vehicle in the dynamic environment and closed-loop detection and re-indoor positioning of the reached position are designed,and the precision and robustness of indoor positioning of the unmanned aerial vehicle are effectively improved.

The present invention relates to a communication protocol converter based on embedded type multi-MCU and a method thereof, wherein the communication protocol converter adopts an embedded type multi-MCU and a real-time multiple task operating system Uc/OS-II. The communication protocol converter realizes the communication to the data collecting I/O module of a device layer through protocols of Nodbus/RTU or ASCII, etc. The measuring data of industrial field is obtained, and then the data is transmitted to remote monitoring systems of upper DAS, DCS, etc. through the protocols of Modbus/TCP, TCP/IP, etc. with an Ethernet communication interface. The functions of relaying and protocol converting in industrial communication network are realized. The serial interface device of the device layer can be easily connected to a monitoring layer network. The embedded type multi-MCU and high-speed RAM data sharing technology are adopted for simplifying circuit design, increasing the reliability and real-time property of system, and facilitating the data exchange, data sharing and information processing between the embedded type MCUs.

The invention discloses a super-high-rise building verticality measuring and controlling method based on a GPS (Global Positioning System). A high-accuracy GPS static measurement mode is adopted, two base stations are placed on a base control network point on the ground, one mobile station is placed on any position of a construction floor surface, a first floor measurement control point is obtained through timing observation, a second floor measurement control point is obtained by using the same method, one of the two control points is used as a station measuring point and the other is used as a rear view point for checking a measuring point, and a floor axis control point is set out after no errors exist. By adopting the invention, accumulated errors in the traditional method can be eliminated, and the verticality of the building structure is improved. In addition, because each measurement is started from a base control network on the ground, no accumulated errors exist, and the maximum error source of the super-high-rise floor verticality control is eliminated.

The invention discloses an indoor positioning navigation system and navigation method for an unmanned forklift. The system comprises a sensor module, multiple UWB (Ultra-wide Bandwidth) positioning base stations, a marker and a server, wherein the sensor module comprises a UWB positioning label and an image collection module; the UWB positioning label transmits a pulse signal; the image collectionmodule collects a real-time image; the multiple UWB positioning base stations communicate with the UWB positioning label and receives a pulse signal of the UWB positioning label; the marker is laid on a designated position of an indoor ground; the server is in communication connection with the image collection module and the UWB positioning base stations; positioning information is obtained by use of data of the UWB positioning base stations; and positioning adjustment data are obtained by use of the position information of the marker. A UWB positioning navigation technology is adopted by theindoor positioning navigation system for the unmanned forklift disclosed by the invention so that the working environment of the unmanned forklift is not influenced by illumination and brightness, and he signal strength is high, and navigation positioning precision is provided; by laying the marker on the ground, UWB positioning accumulative errors are corrected in time and eliminated to implement accurate positioning.

The invention relates to a segmentation method of medical images, and specifically relates to a segmentation method of viscera and internal blood vessels thereof in a surgical planning system. The method designs a segmentation method of images of liver parenchyma, portal veins and hepatic veins based on minimal supervised classification of the three-dimensional CT images of the viscera and internal tubular tissues thereof. The method applies methods of statistics and spatial information, introduces high credible points, and obtains segmentation results based on arrival time of fast march of grey level. The method assumes that spatially adjacent points belong to a same organ, and performs segmentation calculation on the images by using a classification algorithm based on the above assumption, wherein the segmentation calculation comprises the steps of: acquiring estimated values of parameters of a Gaussian mixture distribution model; selecting the high credible points; and calculating the mean value of the grey level of the images and calculating the arrival time by using a fast marching algorithm to obtain arrival time images of three tissue classifications. According to the invention, good robustness and good anti-noise interference capability are obtained, accumulated errors are eliminated effectively, classification accuracy is improved, and great application value in clinical treatment is achieved.

The invention discloses an AGV composite navigation method based on a quick response code and an inertia sensor. The method comprises the following steps: 1) arranging n quick response code labels inan AGV work area with an equal interval; 2) when AGV is at a quick response code site, employing a quick response code navigation mode, using an image identification technology to estimate deviation information from vertical, lateral and directions for performing AGV posture adjustment on the obtained quick response code images; 3) taking the deviation information obtained through the quick response code as an initial deviation of driving inertial navigation of the AGV at the site; and 4) when the AGV drives between two quick response code sites, employing an inertial navigation mode, performing real-time reckoning the deviation of the AGV during a walking process, and realizing the motion of the AGV during adjacent quick response codes. Through equal interval arrangement of the quick response code label, the AGV composite navigation is realized by combining the inertia sensor, the production cost is reduced, on-site enforcement difficulty is reduced, and the navigation precision is increased.

The invention discloses a safety evaluation method for a steep slope and a bending road, and is used for solving the technical problem that the random error and accumulated error are bigger and the road slope can't be measured when data is lost in the existing road slope measurement, and belongs to the field of road safety. The safety evaluation method for the steep slope and the bending road carries out section description and piecewise fitting for tested road sections according to the vehicle-mounted global position system (GPS) measurement information, combines inertial measurement unit (IMU) measurement information to carry out spline interpolation and smoothing processing for road sections with less GPS measurement records when the GPS records are insufficient to carry out the smooth processing or GPS records are lost as drivers drive in the cave, then get the fitting curved surface equation of the roads so as to calculate the turning radius and the slope of the roads. The safety evaluation method for the steep slope and the bending road then moves the roads ahead hop by hop by using recursive estimation, eliminates the influence of cumulative errors in measurement through the limited memory method, improves the calculation precision of geometrical parameters such as the road slope and the curvature, solves the technical problem that the existing roads are difficult to measure, provides the basis for the traffic management department to carry out road surface monitoring and acceptance inspection, and meanwhile proposes load and speed requirements for the safety traffic on the road section.