Inertial navigation system

About: Inertial navigation system is a research topic. Over the lifetime, 14582 publications have been published within this topic receiving 190618 citations. The topic is also known as: intertial guidance system & inertial reference platform.

Inertial navigation sensor integrated obstacle detection system

Abstract: A system that incorporates inertial sensor information into optical flow computations to detect obstacles and to provide alternative navigational paths free from obstacles. The system is a maximally passive obstacle detection system that makes selective use of an active sensor. The active detection typically utilizes a laser. Passive sensor suite includes binocular stereo, motion stereo and variable fields-of-view. Optical flow computations involve extraction, derotation and matching of interest points from sequential frames of imagery, for range interpolation of the sensed scene, which in turn provides obstacle information for purposes of safe navigation.

A Contraction Theory-Based Analysis of the Stability of the Deterministic Extended Kalman Filter

Abstract: The contraction properties of the extended Kalman filter, viewed as a deterministic observer for nonlinear systems, are analyzed. The approach relies on the study of an auxiliary “virtual” dynamical system. Some conditions under which exponential convergence of the state error can be guaranteed are derived. Moreover, contraction provides a simple formalism to study some robustness properties of the filter, especially with respect to measurement errors, as illustrated by a simplified inertial navigation example. This technical note sheds another light on the theoretical properties of this popular observer.

RIDI: Robust IMU Double Integration

Abstract: This paper proposes a novel data-driven approach for inertial navigation, which learns to estimate trajectories of natural human motions just from an inertial measurement unit (IMU) in every smartphone. The key observation is that human motions are repetitive and consist of a few major modes (e.g., standing, walking, or turning). Our algorithm regresses a velocity vector from the history of linear accelerations and angular velocities, then corrects low-frequency bias in the linear accelerations, which are integrated twice to estimate positions. We have acquired training data with ground truth motion trajectories across multiple human subjects and multiple phone placements (e.g., in a bag or a hand). The qualitatively and quantitatively evaluations have demonstrated that our simple algorithm outperforms existing heuristic-based approaches and is even comparable to full Visual Inertial navigation to our surprise. As far as we know, this paper is the first to introduce supervised training for inertial navigation, potentially opening up a new line of research in the domain of data-driven inertial navigation. We will publicly share our code and data to facilitate further research (Project website: https://yanhangpublic.github.io/ridi).

Opportunistic radio SLAM for indoor navigation using smartphone sensors

Abstract: This paper provides the experimental results of a system utilising only the sensors available on a smartphone to provide an indoor positioning system that does not require any prior knowledge of floor plans, transmitter locations, radio signal strength databases, etc. The system utilises a Distributed Particle Filter Simultaneous Localisation and Mapping (DPSLAM) method to provide constraints on the drift of a simple hip-mounted Inertial Measurement Unit (IMU) integrated into the smartphone and providing the core information on the movement of the user. This system was developed during a project investigating methods of providing relative positioning systems to a team operating for extended periods without GPS. The paper concentrates on the DPSLAM positioning technique suitable for use by an individual with no prior knowledge of the area of operation before deployment. As with all SLAM systems, the user is simply required to revisit locations periodically to enable IMU drifts to be observed and corrected.

The Development of an UAV Borne Direct Georeferenced Photogrammetric Platform for Ground Control Point Free Applications

Abstract: To facilitate applications such as environment detection or disaster monitoring, the development of rapid low cost systems for collecting near real time spatial information is very critical. Rapid spatial information collection has become an emerging trend for remote sensing and mapping applications. In this study, a fixed-wing Unmanned Aerial Vehicle (UAV)-based spatial information acquisition platform that can operate in Ground Control Point (GCP) free environments is developed and evaluated. The proposed UAV based photogrammetric platform has a Direct Georeferencing (DG) module that includes a low cost Micro Electro Mechanical Systems (MEMS) Inertial Navigation System (INS)/ Global Positioning System (GPS) integrated system. The DG module is able to provide GPS single frequency carrier phase measurements for differential processing to obtain sufficient positioning accuracy. All necessary calibration procedures are implemented. Ultimately, a flight test is performed to verify the positioning accuracy in DG mode without using GCPs. The preliminary results of positioning accuracy in DG mode illustrate that horizontal positioning accuracies in the x and y axes are around 5 m at 300 m flight height above the ground. The positioning accuracy of the z axis is below 10 m. Therefore, the proposed platform is relatively safe and inexpensive for collecting critical spatial information for urgent response such as disaster relief and assessment applications where GCPs are not available.