Nothing Special   »   [go: up one dir, main page]

Yan et al., 2019 - Google Patents

Ronin: Robust neural inertial navigation in the wild: Benchmark, evaluations, and new methods

Yan et al., 2019

View PDF
Document ID
3662396333030898342
Author
Yan H
Herath S
Furukawa Y
Publication year
Publication venue
arXiv preprint arXiv:1905.12853

External Links

Snippet

This paper sets a new foundation for data-driven inertial navigation research, where the task is the estimation of positions and orientations of a moving subject from a sequence of IMU sensor measurements. More concretely, the paper presents 1) a new benchmark containing …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/10—Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in preceding groups by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals, or other moving solid bodies, e.g. using odometers, using pedometers
    • G01C22/006—Pedometers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C17/00—Compasses; Devices for ascertaining true or magnetic north for navigation or surveying purposes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality

Similar Documents

Publication Publication Date Title
Yan et al. Ronin: Robust neural inertial navigation in the wild: Benchmark, evaluations, and new methods
Herath et al. Ronin: Robust neural inertial navigation in the wild: Benchmark, evaluations, & new methods
Chen et al. Oxiod: The dataset for deep inertial odometry
Chen et al. Ionet: Learning to cure the curse of drift in inertial odometry
Sun et al. IDOL: Inertial deep orientation-estimation and localization
Chen et al. Deep neural network based inertial odometry using low-cost inertial measurement units
Shen et al. Closing the gaps in inertial motion tracking
Yan et al. RIDI: Robust IMU double integration
US11788843B2 (en) Determining the location of a mobile device
US8930163B2 (en) Method for step detection and gait direction estimation
Wang et al. Pose-invariant inertial odometry for pedestrian localization
Chen et al. Deep learning for inertial positioning: A survey
Rao et al. Ctin: Robust contextual transformer network for inertial navigation
Lee et al. A fast quaternion-based orientation optimizer via virtual rotation for human motion tracking
KR20200091709A (en) Electronic apparatus and control method thereof
Manos et al. Walking direction estimation using smartphone sensors: A deep network-based framework
Fu et al. A survey on artificial intelligence for pedestrian navigation with wearable inertial sensors
Wu et al. Indoor positioning method for pedestrian dead reckoning based on multi-source sensors
Zhang et al. Mag-ODO: Motion speed estimation for indoor robots based on dual magnetometers
Zeinali et al. Imunet: Efficient regression architecture for inertial imu navigation and positioning
Elyasi et al. Step length is a more reliable measurement than walking speed for pedestrian dead-reckoning
Zeinali et al. Imunet: Efficient regression architecture for imu navigation and positioning
Herath Robust neural inertial navigation in the wild
Wang et al. From imu measurement sequence to velocity estimate sequence: An effective and efficient data-driven inertial odometry approach
Fu et al. Analysis of the recent ai for pedestrian navigation with wearable inertial sensors