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.

Advances in Atomic Gyroscopes: A View from Inertial Navigation Applications

Abstract: With the rapid development of modern physics, atomic gyroscopes have been demonstrated in recent years. There are two types of atomic gyroscope. The Atomic Interferometer Gyroscope (AIG), which utilizes the atomic interferometer to sense rotation, is an ultra-high precision gyroscope; and the Atomic Spin Gyroscope (ASG), which utilizes atomic spin to sense rotation, features high precision, compact size and the possibility to make a chip-scale one. Recent developments in the atomic gyroscope field have created new ways to obtain high precision gyroscopes which were previously unavailable with mechanical or optical gyroscopes, but there are still lots of problems that need to be overcome to meet the requirements of inertial navigation systems. This paper reviews the basic principles of AIG and ASG, introduces the recent progress in this area, focusing on discussing their technical difficulties for inertial navigation applications, and suggests methods for developing high performance atomic gyroscopes in the near future.

IONet: Learning to Cure the Curse of Drift in Inertial Odometry

Abstract: 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 when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques.

Guidance and Relative Navigation for Autonomous Rendezvous in a Circular Orbit

Abstract: Algorithmsforautonomousguidanceofspacecraftto approach,toe yaround,andtodepartfroma targetvehicle ina circularorbitarepresented. Thealgorithmsarebasedon theclosed-form solution of linearClohessy ‐Wiltshire equations. The approach and departure algorithms are adaptations of the glideslope guidance used in the past for rendezvous and proximity operations of the space shuttle with other vehicles with astronauts in the guidance loop. The multipulse glideslope algorithms are general, capable of effecting a translation motion of spacecraft in any direction in space autonomously, decelerating while approaching a target or a nearby location, and accelerating while receding. The e yaround algorithm enables the spacecraft to circumnavigate a target spacecraft in any plane, the orbit plane and the local horizontal plane being two special cases thereof. The circumnavigation is performed in a specie ed period using a specie ed number of pulses; the larger the number of pulses, the smaller the deviation of e yaround from the specie ed radius of circumnavigation. The implementation of these algorithms requires estimates of position and velocity of the spacecraft relative to the target. This relative navigation is performed with an extended Kalman e lter using range and angle measurements of the target relative to the spacecraft focal plane and the spacecraft attitude estimates from an inertial navigation system. The corresponding measurement models and process noise matrix are provided. Several scenarios are simulated to illustrate the guidance algorithms and relative navigation.

Low-cost flight control system for a small autonomous helicopter

Abstract: In this paper we describe a low-cost flight control system for a small (60 class) helicopter which is part of a larger project to develop an autonomous flying vehicle. Our approach differs from that of others in not using an expensive inertial/GPS sensing system. The primary sensors for vehicle stabilization are a low-cost inertial sensor and a pair of CMOS cameras. We describe the architecture of our flight control system, the inertial and visual sensing subsystems and present some flight control results.

Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors

Abstract: This article describes a method of navigation for an individual based on traditional inertial navigation system (INS) technology, but with very small and self-contained sensor systems. A conventional INS contains quite accurate, but large and heavy, gyroscopes and accelerometers, and converts the sensed rotations and accelerations into position displacements through an algorithm known as a strapdown navigator. They also, almost without exception, use an error compensation scheme such as a Kalman filter to reduce the error growth in the inertially sensed motion through the use of additional position and velocity data from GPS receivers, other velocity sensors (e.g., air, water, and ground speed), and heading aids such as a magnetic compass. This technology has been successfully used for decades, yet the size, weight, and power requirements of sufficiently accurate inertial systems and velocity sensors have prevented their adoption for personal navigation systems. Now, however, as described in this article, miniature inertial measurement units (IMUs) as light as a few grams are available. When placed on the foot to exploit the brief periods of zero velocity when the foot strikes the ground (obviating the need for additional velocity measurement sensors), these IMUs allow the realization of a conventional Kalman-filter-based aided strapdown inertial navigation system in a device no larger or heavier than a box of matches. A particular advantage of this approach is that no stride modeling is involved with its inherent reliance on the estimation of a forward distance traveled on every step — the technique works equally well for any foot motion, something especially critical for soldiers and first responders. Also described is a technique to exploit magnetic sensor orientation data even in indoor environments where local disturbances in the Earth’s magnetic field are significant. By carefully comparing INSderived and magnetically derived heading and orientation, a system can automatically deter