12 results about "Pedestrian navigation" patented technology

The present application relates to a kind of pedestrian collaborative navigation method based on distance and angle, the collaborative navigation method includes: S1, by MIMU output each pedestrian respective three-axis accelerometer and three-axis gyroscope data, and carry out strapdown inertial navigation solution, again by the state of zero speed detection to the data of inertial navigation solution is carried out zero speed correction by extended Kalman filtering;S2, by UWB ranging angle measurement obtains the relative distance and angle between pedestrians as observation, then with the position information and heading angle information of N pedestrians as state quantity, carries out Kalman filtering, obtains the correction data of the position information and heading angle information of each pedestrian;S3, according to the position information and heading angle information of each pedestrian after correction collaborative navigation is carried out.The present application uses distance and angle as observation to correct pedestrian navigation after ZUPT auxiliary, solves the current pedestrian navigation heading angle drift and the problem of large amount of calculation of centralized filtering.

The present disclosure relates to a pedestrian navigation heading constraint method, device, electronic equipment, storage medium and program product. The method comprises: obtaining inertial data of a target pedestrian; based on the inertial data, extracting the heading of the target pedestrian at the current time; in response to the heading of the target pedestrian within a first sliding window satisfying a first preset straight line condition, based on the difference information between the heading at the current time and the heading of the target pedestrian within the first sliding window, and / or the error distribution of the heading of the target pedestrian within a second sliding window, performing heading constraint processing on the target pedestrian at the current time, wherein the right boundaries of the first sliding window and the second sliding window are both the current time, and the length of the second sliding window is greater than the length of the first sliding window.

The application relates to the technical field of pedestrian navigation, in particular to a pedestrian positioning method and system based on building structured information assistance, which comprises the following steps: initializing a track angle through a dynamic window self-adaptive track angle initialization method according to pedestrian motion structured features; judging straight movement and turning according to the pedestrian motion structured features and the initial track angle; performing multi-dimensional error collaborative correction through an improved self-adaptive Kalman filter; performing multi-dimensional collaborative correction through a flexible constraint model and a state-by-state Kalman filter when turning; dynamically compensating state quantities after straight movement correction and state quantities after turning correction according to environmental interference structured features; obtaining compensated state quantities; and positioning the pedestrian through the compensated state quantities. The application can fully exploit multi-dimensional structured information, dynamically optimize correction strategies for different motion states, and realize adaptive compensation of environmental interference for pedestrian positioning.

The invention provides a traffic pedestrian navigation method and system based on multi-source information fusion, and relates to the technical field of navigation and positioning. Inputting the traffic scene multi-source information and the pedestrian historical movement data into a pedestrian intention prediction model, and estimating the potential destination or path selection tendency of the pedestrian; based on a pedestrian intention prediction result, dynamically distributing and adaptively adjusting a fusion weight of each piece of multi-source information through a fuzzy evaluation model, and carrying out fusion processing on the multi-source information by utilizing the adjusted fusion weight to generate comprehensive navigation data; and inputting the integrated navigation data and the pedestrian intention prediction result into a path planning model based on reinforcement learning to generate a personalized navigation path. According to the invention, by introducing a pedestrian intention prediction mechanism, the dynamic optimization of the multi-source information fusion weight is realized, and the accuracy and adaptability of pedestrian navigation in a complex urban environment are effectively improved.

This invention discloses a path navigation method, device, and medium for mobile robots in densely populated environments. The method includes identifying objects in the densely populated environment and constructing semantic map information based on these objects; performing semantic information analysis on the objects and using a unified interaction space method to describe the human-machine-object relationship to construct an interaction space model; constructing a sampling path tree by grafting several sampling paths to provide sampling paths with a reasonable density based on the needs of the densely populated environment; using different sampling path time allocations based on different navigation modes to simulate different pedestrian navigation behaviors; performing collision detection and comfort assessment on the sampling paths; setting evaluation indicators and comprehensively evaluating and filtering the sampling paths based on these indicators to obtain the sampling path corresponding to the best evaluation indicator as the optimal navigation path. This invention predicts pedestrian movement trajectories in real time and quickly updates path planning, ensuring safe navigation of the mobile robot in dynamic environments.

Systems, methods, and other embodiments described herein relate to providing navigational guidance to a pedestrian on infrastructure elements or vehicles in the vicinity of the pedestrian based on the pedestrian being identified as unfamiliar with an environment. In one embodiment, a method includes determining that a pedestrian is in an unfamiliar area based on sensor data that characterizes the pedestrian. The method also includes acquiring a navigational instruction presented on a user device of the pedestrian. The method further includes presenting navigational assistance on an electronic display in a vicinity of the pedestrian responsive to an indication that the pedestrian is in the unfamiliar area. The navigational assistance is based on the navigational instruction.

The application claims a kind of strapdown inertial navigation pedestrian three-dimensional position correction method, belongs to the field of inertial pedestrian navigation, it includes the following steps: pedestrian height correction, the height of fusion by barometer and accelerometer independently solved height, the initial correction of pedestrian height is carried out;Pedestrian pitch angle correction, the relationship between step height and pitch angle of pedestrian step length is used to calculate pitch angle, and the pitch angle is fused with attitude solution pitch angle, and the pitch angle is corrected;Pedestrian three-dimensional position correction, the relationship between pedestrian pitch angle, step length and heading angle is used to correct the three-dimensional position of pedestrian.The position information of pedestrian in three-dimensional space in strapdown inertial navigation system is corrected gradually by the above three sub-algorithms according to the application.The method disclosed by the application can realize the accurate positioning of pedestrian position in three-dimensional space, which can not only be applied to the existing city high-rise building such a more regular experimental environment pedestrian inertial navigation, especially suitable for more extensive irregular environment such as ramp, mountainous environment.

The application discloses an indoor pedestrian navigation method and system based on behavior probability analysis, wherein the method steps comprise the following steps: based on the measurement result of an IMU sensor, a Kalman filter is used to predict the current state of an indoor pedestrian, and a prediction result is obtained; a gait detection method is used to extract a maximum probability zero speed point of the indoor pedestrian; a stair step probability detection method is used to estimate the height of the pedestrian climbing stairs, and an estimation result is obtained; and the prediction result is corrected by using the maximum probability zero speed point and the estimation result, so that indoor pedestrian navigation is completed. The application extracts the zero speed point based on the maximum probability, is suitable for different gaits, and reduces the influence of unstable barometer output on height correction. Through experimental verification, the application can realize high positioning accuracy in an indoor scene.

This invention discloses a pedestrian navigation method based on the RANSAC algorithm, relating to the field of pedestrian navigation. The method collects acceleration and angular velocity data during pedestrian movement using an inertial measurement unit (IMU). First, it calculates the sensor variance within a sliding window and dynamically adjusts the adaptive threshold for determining a stationary state. Then, it uses the RANSAC algorithm for multiple rounds of random sampling and model validation. By constructing a stationary state model, it filters interior points, retaining the model with the most interior points and determining the stationary moment based on the proportion of interior points. When stationary is determined, it triggers Zero Velocity Correction (ZUPT) and combines it with Kalman filtering to achieve navigation and positioning. The process noise covariance matrix and observation noise covariance matrix of the Kalman filter can be adaptively adjusted during operation. This invention solves the problem of traditional threshold methods being susceptible to noise and outlier interference, significantly improving the robustness of stationary state detection and the positioning accuracy of pedestrian navigation.

The invention discloses a positioning error compensation method influenced by pedestrian body shielding, and belongs to the technical field of indoor high-precision positioning. The method comprises the following steps: judging a body attachment position by using an IMU (Inertial Measurement Unit) and a sound sensor of a positioning terminal; the spatial relationship among the terminal, the human body and the UWB base station is deduced by combining the course and the historical position of the pedestrian; identifying a UWB base station shielded by a human body based on the simplified body geometric model; for the shielded base station, according to the thickness and angle of the signal penetrating through the human body, a pre-calibrated human body shielding error model is utilized to calculate a theoretical distance measurement error and compensate the theoretical distance measurement error; and finally, carrying out position calculation by using the compensated distance measurement value. According to the method, the problem of systematic distance measurement positive deviation caused by body shielding in UWB pedestrian positioning is specifically solved, the positioning precision and robustness are remarkably improved through multi-source information fusion and modeling compensation, and the method is suitable for indoor pedestrian navigation and monitoring scenes with high precision requirements.

The invention relates to the technical field of positioning and navigation, in particular to a multi-dimensional information constrained adaptive dead reckoning positioning method. The method comprises the following steps: firstly, acquiring acceleration, geomagnetism and gyroscope data, and preprocessing the acquired data; secondly, performing step number detection by using a peak detection method with peak-valley pairing constraint of an acceleration threshold value and a time threshold value, calculating a step length by adopting an Adam optimization step length estimation algorithm based on multi-dimensional information constraint, and performing heading estimation by adopting a method of fusing Sage-Husa adaptive filtering of H infinity constraint with gyroscope and magnetometer data; finally, continuous dead reckoning is carried out on pedestrian displacement, and low-cost and high-robustness pedestrian navigation positioning is achieved.

1. The name of the design product: the graphical user interface of the electronic device for walking navigation display. 2. The use of the design product: the design product is used for an electronic device. 3. The design points of the design product: the interface content of the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 interface change state figure 1. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: an interactive interface for displaying navigation information. 7. The change state of the graphic user interface is illustrated as follows: the interface of the interactive interface for displaying navigation information, the main view of design 1 is the interface appeared after the user touches the touch area of the electronic device (such as the touch bar of the leg of AR glasses) and inputs the starting mode and destination by voice; the interface change state diagram 1 of design 1 is the interface displayed after the electronic device enters the “walking navigation” mode from the main view of design 1 after data processing, in which the preview information of walking to the destination input by voice is displayed, including the walking route, walking time and distance, etc.; the interface change state diagram 2 of design 1 is the interface displayed after the electronic device enters the walking navigation from the interface change state diagram 1 of design 1 by clicking the touch area of the electronic device (such as single-clicking the touch bar of the leg of AR glasses); the interface change state diagram 3 of design 1 is the interface displayed after jumping from the interface change state diagram 2 of design 1 when navigating to the place where turning is needed; the main view of design 2 is the interface appeared after the user touches the touch area of the electronic device (such as the touch bar of the leg of AR glasses) and inputs the starting mode and destination by voice; the interface change state diagram 1 of design 2 is the interface displayed after the electronic device enters the “walking navigation” mode from the main view of design 2 after data processing, in which the preview information of walking to the destination input by voice is displayed, including the walking route, walking time and distance, etc.; the interface change state diagram 2 of design 2 is the interface displayed after the electronic device enters the walking navigation from the interface change state diagram 1 of design 2 by clicking the touch area of the electronic device (such as single-clicking the touch bar of the leg of AR glasses); the interface change state diagram 3 of design 2 is the interface displayed after jumping from the interface change state diagram 2 of design 2 when navigating to the intersection in the red light state; the main view of design 3 is the interface appeared after the user touches the touch area of the electronic device (such as the touch bar of the leg of AR glasses) and inputs the starting mode and destination by voice; the interface change state diagram 1 of design 3 is the interface displayed after the electronic device enters the “walking navigation” mode from the main view of design 3 after data processing, in which the preview information of walking to the destination input by voice is displayed, including the walking route, walking time and distance, etc.; the interface change state diagram 2 of design 3 is the interface displayed after the electronic device enters the walking navigation from the interface change state diagram 1 of design 3 by clicking the touch area of the electronic device (such as single-clicking the touch bar of the leg of AR glasses); the interface change state diagram 3 of design 3 is the interface displayed after jumping from the interface change state diagram 2 of design 3 when navigating to the end or ending the navigation by double-clicking the touch area of the electronic device (such as the touch bar of the leg of AR glasses).