US-12618675-B2 - Method and system for magnetic-based collaborative positioning among portable devices

US12618675B2US 12618675 B2US12618675 B2US 12618675B2US-12618675-B2

Abstract

Magnetic-based collaborative positioning of a portable device involves obtaining magnetic field measurements for the portable device, obtaining magnetic fingerprint map information, obtaining parameters of motion of the portable device, obtaining collaborative assistance data from at least one neighbor portable device and determining position of the portable device based on the obtained magnetic field measurements, the obtained magnetic map information, the obtained motion parameters and the obtained collaborative assistance data.

Inventors

Gennadii Berkovich
Dmitry Churikov
Iurii Kotik
Vladimir Pentiukhov
Christopher Goodall

Assignees

INVENSENSE, INC.

Dates

Publication Date: 20260505
Application Date: 20211027

Claims (19)

1 . A method for magnetic-based collaborative positioning of a portable device, the method comprising: a) obtaining Earth's magnetic field measurements from a magnetometer associated with the portable device; b) obtaining magnetic fingerprint map information; c) obtaining parameters of motion of the portable device; d) obtaining with the portable device collaborative assistance data directly from at least one neighbor portable device, wherein the collaborative assistance data comprises a state estimation of the at least one neighbor portable device and at least one of a relative distance between the portable device and the at least one neighbor portable device and a relative direction between the portable device and the at least one neighbor portable device; and e) determining position of the portable device with at least one processor of the portable device based on the obtained magnetic field measurements, the obtained magnetic fingerprint map information, the obtained motion parameters, and the obtained collaborative assistance data, wherein determining position of the portable device comprises maintaining position tracking for the portable device and wherein the collaborative assistance data is applied during an update phase of a particle filter.
2 . The method of claim 1 , wherein determining position of the portable device comprises at least one of: a) starting position tracking for the portable device during initialization; and b) reacquiring position tracking for the portable device after loss of tracking.
3 . The method of claim 2 , wherein determining position of the portable device comprises starting position tracking for the portable device during an initialization phase of a particle filter and wherein an initialization area is narrowed based at least in part on the collaborative assistance data.
4 . The method of claim 2 , wherein determining position of the portable device comprises reacquiring position tracking for the portable device after loss of tracking wherein the collaborative assistance for reacquisition is applied during a prediction step of a particle filter and wherein a proportion of particles are sampled from a distribution of the collaborative assistance data and a remainder of particles are propagated according to a motion model.
5 . The method of claim 1 , wherein the state estimation of the at least one neighbor portable device comprises a mean position and an uncertainty.
6 . The method of claim 1 , wherein the state estimation of the at least one neighbor portable device comprises a set of particles with corresponding weights.
7 . The method of claim 1 , further comprising determining whether a distribution of the state estimation of the at least one neighbor portable device is unimodal.
8 . The method of claim 7 , wherein the state estimation of the at least one neighbor portable device comprises a mean position and an uncertainty when a distribution of the state is unimodal and comprises a set of particles with corresponding weights when a distribution of the state is multimodal.
9 . The method of claim 1 , further comprising obtaining collaborative assistance data from a plurality of neighbor portable devices.
10 . The method of claim 9 , further comprising preliminarily selecting a set of at least one neighbor portable device from the plurality of neighbor portable devices.
11 . The method of claim 10 , wherein selecting a set of at least one neighbor portable device is based at least in part on any one or any combination of: a) recency of data of the neighbor portable devices; b) line of sight to the neighbor portable devices; c) proximity of the neighbor portable devices; d) stationarity of the neighbor portable devices; e) uncertainty of position of the neighbor portable devices; f) uncertainty of relative measurements of the neighbor portable devices; and g) dilution of precision of the neighbor portable devices.
12 . The method of claim 1 , wherein the obtained collaborative assistance data are propagated to a first instant and wherein the position determined for the portable device is for the first instant.
13 . The method of claim 1 , wherein the position for the portable device is determined using a state estimation technique that comprises a Kalman filter for estimating a linear state for magnetometer bias and a particle filter for estimating a non-linear state for position of the portable device.
14 . The method of claim 1 , wherein the determined magnetic-based collaborative positioning is combined with at least one or a combination of: i) map-matching; ii) radio frequency fingerprinting; iii) proximity sensing; and iv) an optical technique.
15 . A portable device for magnetic-based collaborative positioning, comprising: a) a magnetometer outputting Earth's magnetic field measurements for the portable device; b) a wireless communication module for obtaining magnetic fingerprint map information; c) motion sensors outputting parameters of motion of the portable device; d) a wireless communication module for obtaining collaborative assistance data directly from at least one neighbor portable device, wherein the collaborative assistance data comprises a state estimation of the at least one neighbor portable device and at least one of a relative distance between the portable device and the at least one neighbor portable device and a relative direction between the portable device and the at least one neighbor portable device; and e) at least one processor configured to: i) receive the magnetic field measurements for the portable device, the magnetic fingerprint map information, the motion parameters of the portable device and the collaborative assistance data; and ii) determine position of the portable device based on the received magnetic field measurements, the received magnetic fingerprint map information, the received motion parameters, and the received collaborative assistance data, wherein determining position of the portable device comprises maintaining position tracking for the portable device and wherein the collaborative assistance data is applied during an update phase of a particle filter.
16 . The portable device of claim 15 , wherein the at least one processor is configured to determine position of the portable device by at least one of: a) start position tracking for the portable device during initialization; b) reacquire position tracking for the portable device after loss of tracking; and c) maintain position tracking for the portable device.
17 . A system for magnetic-based collaborative positioning, comprising: a) a server providing magnetic fingerprint map information; and b) a plurality of neighbor portable devices, wherein each portable device comprises: i) a magnetometer outputting Earth's magnetic field measurements for the portable device; ii) a wireless communication module for obtaining magnetic fingerprint map information; iii) motion sensors outputting parameters of motion of the portable device; iv) a wireless communication module for obtaining collaborative assistance data directly from at least one neighbor portable device, wherein the collaborative assistance data comprises a state estimation of the at least one neighbor portable device and at least one of a relative distance between the portable device and the at least one neighbor portable device and a relative direction between the portable device and the at least one neighbor portable device; and v) at least one processor configured to: I) receive the magnetic field measurements for the portable device, the magnetic fingerprint map information, the motion parameters of the portable device and the collaborative assistance data; and II) determine position of the portable device based on the received magnetic field measurements, the received magnetic fingerprint map information, the received motion parameters, and the received collaborative assistance data, wherein determining position of the portable device comprises maintaining position tracking for the portable device and wherein the collaborative assistance data is applied during an update phase of a particle filter.
18 . The system of claim 17 , wherein at least one of the plurality of neighbor portable devices receives collaborative assistance from another of the plurality of neighbor devices by communicating through the server.
19 . The system of claim 17 , wherein at least one of the plurality of neighbor portable devices receives collaborative assistance data directly from at least one other of the plurality of neighbor devices.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority from and benefit of U.S. Provisional Patent Application Ser. No. 63/108,210, filed Oct. 30, 2020, which is entitled “Magnetic-based collaborative positioning,” is assigned to the assignee hereof, and is incorporated by reference in its entirety. FIELD OF THE PRESENT DISCLOSURE The present disclosure relates to positioning of people, vehicles and goods within indoor environments. More specifically, systems and methods are provided for magnetic-based techniques using portable devices. BACKGROUND A variety of technologies have been developed to help determine position information for moving platforms, such as autonomous or piloted ground or aerial vehicles. A pedestrian is also considered a moving platform. For example, the Global Navigation Satellite Systems (GNSS) comprises a group of satellites that transmit encoded signals and receivers on the ground, by means of trilateration techniques, can calculate their position using the travel time of the satellites' signals and information about the satellites' current location. GNSS is considered a reference-based technique and provides an absolute source of navigational information. The desirability of obtaining position information extends to indoor environments where pedestrians may traverse and multiple types of vehicles may be employed, including forklifts and other service machinery in a warehouse, automobiles and buses in underground parking structures, wheelchairs in a hospital, and portable robots or unmanned aerial vehicles (UAV)/drones that navigate within buildings. However, GNSS is typically unsatisfactory when use for indoor positioning due to the attenuation of satellite signals in the walls and roofs buildings, necessitating use of different technologies. In the absence of GNSS signals in indoor environments, an Inertial Navigation System (INS) may be used by employing techniques such as dead reckoning to help determine position. INS is a self-contained and/or “non-reference based” technique that utilizes inertial sensors within the moving object and do not depend upon external sources of information that can become interrupted or blocked. Motion sensors are self-contained within the moving object and measure motion, such as through the use of gyroscopes to measure the object's rate of rotation/angle and accelerometers to measure the object's specific force (from which acceleration is obtained). Using initial estimates of position, velocity and orientation angles of the moving object as a starting point, the INS readings can subsequently be integrated over time and used to determine a navigation solution. Typically, measurements are integrated (mathematical integration which is a calculus operation) once for gyroscopes to yield orientation angles and twice for accelerometers to yield position of the moving object incorporating the orientation angles. Thus, sensor measurements will undergo a triple integration operation during the process of yielding position. Integrated navigation techniques usually integrate (i.e. combine) reference-based or absolute navigational information with self-contained or non-reference based navigation information. Integrated navigation techniques may employ state estimation techniques, such as a Kalman filter, an extended Kalman filter, a Gaussian sum filter, an unscented filter, a particle filter, or others, which have characteristics including a prediction phase and an update phase (which may also be termed a measurement update phase). A state estimation technique also uses a system model and measurement model(s) based on what measurements are used. The system model is used in the prediction phase, and the measurement model(s) is/are used in the update phase. Due to the integration operations (the mathematical integration as in calculus), motion sensor-based techniques may fail to provide adequate performance by themselves, particularly over longer durations due to significant performance degradation from accumulating sensor drifts and bias. As such, positioning technologies relying solely on motion sensors may not satisfy all requirements for seamless indoor navigation applications. As a result, alternative positioning techniques that can provide strong coverage in areas where access to GNSS and other reference-based positioning is degraded or denied are desirable. One class of techniques is known as “fingerprinting,” and relies on recording patterns of electromagnetic signals at known locations within an area for which position information may be desired. When a device subsequently measures a pattern of received signals that is correlated with a known location, that location may be used to determine the position of the device and/or to aid another positioning technique, such as through integration with the INS techniques noted above. A suitable example of signals that may be used for fingerprinting may be based on the communication signals for a