Chen et al., 2018 - Google Patents
Ionet: Learning to cure the curse of drift in inertial odometryChen et al., 2018
View PDF- Document ID
- 2470342450088335853
- Author
- Chen C
- Lu X
- Markham A
- Trigoni N
- Publication year
- Publication venue
- Proceedings of the AAAI Conference on Artificial Intelligence
External Links
Snippet
Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error …
- 235000014277 Clidemia hirta 0 title description 6
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
- G01C21/165—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 combined with non-inertial navigation instruments
-
- 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
- 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
- G01C21/26—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in preceding groups specially adapted for navigation in a road network with correlation of data from several navigational instruments
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Chen et al. | Ionet: Learning to cure the curse of drift in inertial odometry | |
Chen et al. | Deep neural network based inertial odometry using low-cost inertial measurement units | |
US11788843B2 (en) | Determining the location of a mobile device | |
Chen et al. | Oxiod: The dataset for deep inertial odometry | |
Yan et al. | Ronin: Robust neural inertial navigation in the wild: Benchmark, evaluations, and new methods | |
Esfahani et al. | OriNet: Robust 3-D orientation estimation with a single particular IMU | |
Solin et al. | Inertial odometry on handheld smartphones | |
Norrdine et al. | Step detection for ZUPT-aided inertial pedestrian navigation system using foot-mounted permanent magnet | |
Chen et al. | Deep learning for inertial positioning: A survey | |
Kourogi et al. | A method of pedestrian dead reckoning using action recognition | |
Wang et al. | Pose-invariant inertial odometry for pedestrian localization | |
CN110207692B (en) | A map-aided inertial pre-integration pedestrian navigation method | |
Lu et al. | Hybrid navigation method of INS/PDR based on action recognition | |
Yu et al. | AZUPT: Adaptive zero velocity update based on neural networks for pedestrian tracking | |
Van Nguyen et al. | Real-time human foot motion localization algorithm with dynamic speed | |
Chen et al. | Contrastive learning of zero-velocity detection for pedestrian inertial navigation | |
Saadatzadeh et al. | An improvement in smartphone-based 3D indoor positioning using an effective map matching method | |
Tang et al. | IC-GVINS: A robust, real-time, INS-centric GNSS-visual-inertial navigation system for wheeled robot | |
Kessler et al. | Multi-Sensor indoor pedestrian navigation system with vision aiding | |
Hesch et al. | A 3d pose estimator for the visually impaired | |
Etzion et al. | MoRPI: Mobile robot pure inertial navigation | |
Chen | Learning methods for robust localization | |
US20240271938A1 (en) | Smartphone-based inertial odometry | |
Chen et al. | Improved window segmentation for deep learning based inertial odometry | |
Derrode et al. | Unsupervised pedestrian trajectory reconstruction from IMU sensors |