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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.


Papers
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Proceedings ArticleDOI
TL;DR: In this article, the first inertial measurements with an atomic accelerometer onboard an aircraft were reported, along both the horizontal and vertical axes of the aircraft with one-shot sensitivities of $2.3 \times 10-4 \times 4 \times 1.1 \sim 0.1
Abstract: Inertial sensors based on cold atom interferometry exhibit many interesting features for applications related to inertial navigation, particularly in terms of sensitivity and long-term stability. However, at present the typical atom interferometer is still very much an experiment---consisting of a bulky, static apparatus with a limited dynamic range and high sensitivity to environmental effects. To be compliant with mobile applications further development is needed. In this work, we present a compact and mobile experiment, which we recently used to achieve the first inertial measurements with an atomic accelerometer onboard an aircraft. By integrating classical inertial sensors into our apparatus, we are able to operate the atomic sensor well beyond its standard operating range, corresponding to half of an interference fringe. We report atom-based acceleration measurements along both the horizontal and vertical axes of the aircraft with one-shot sensitivities of $2.3 \times 10^{-4}\,g$ over a range of $\sim 0.1\,g$. The same technology can be used to develop cold-atom gyroscopes, which could surpass the best optical gyroscopes in terms of long-term sensitivity. Our apparatus was also designed to study multi-axis atom interferometry with the goal of realizing a full inertial measurement unit comprised of the three axes of acceleration and rotation. Finally, we present a compact and tunable laser system, which constitutes an essential part of any cold-atom-based sensor. The architecture of the laser is based on phase modulating a single fiber-optic laser diode, and can be tuned over a range of 1 GHz in less than 200 $\mu$s.

49 citations

Journal ArticleDOI
TL;DR: This paper proposes a robust orientation algorithm with the total least squares to provide the orientation constraint for the integrating system, and develops the Kalman filter to integrate these constraints with the inertial navigation results to estimate the orientation and position for the ground vehicle.
Abstract: This paper presents a novel multi-sensor navigation system for the urban ground vehicle. The integration system can combine the measurements from a polarized skylight sensor, an inertial sensor, and a monocular camera. Utilizing the polarized skylight sensor, we propose a robust orientation algorithm with the total least squares to provide the orientation constraint for the integrating system. In our algorithm, the ambiguity problem of polarized orientation is solved without any other sensor. In order to enhance the algorithm's robustness in the urban environment, we also propose a real-time method that uses the gradient of the degree of the polarization to remove the obstacles. With a monocular camera, we build a metric map and recognize places in the map to provide the position constraint for the integrating system. We develop the Kalman filter to integrate these constraints with the inertial navigation results to estimate the orientation and position for the ground vehicle. The results demonstrate that our proposed system outperforms other vision-based navigation algorithms-the RMSE of the position error is 2.04 m (0.01% of the travelled distance) and the RMSE of the orientation error is 0.84°. Finally, we present interesting insights gained with respect to the further work in sensors and robotics.

49 citations

Proceedings ArticleDOI
19 Mar 2009
TL;DR: A novel combination of methods is proposed to increase the distance between positions where absolute repositioning is still mandatory, and the prototype of a self-contained indoor positioning system is presented.
Abstract: State-of-the-art indoor positioning systems are based on short-range wireless technologies such as ultra-wideband (UWB) and wireless local area network (WLAN). Additional information produced by a low-cost inertial measurement unit (IMU) or selected from low-grade floor plans is frequently used to improve the positioning accuracy. Therefore absolute and relative positioning systems (e.g. WLAN and IMU) are typically integrated using a Kalman filter (KF) or a particle filter (PF). In this case, all the buildings have to be equipped with a large number of transmitters and receivers which may render inoperable during emergency situations and induce substantial costs both in terms of setup and maintenance. Hence, in this paper we present and assess the prototype of a self-contained indoor positioning system. Our prototype consists of a micro-electro-mechanical system- (MEMS) based IMU and a mobile computer which includes a database with characteristic building information. A novel combination of methods is proposed to increase the distance between positions where absolute repositioning is still mandatory.

49 citations

Journal ArticleDOI
TL;DR: A universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise.
Abstract: Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

49 citations

Journal ArticleDOI
TL;DR: A GPS-free localization framework that uses two-way time of arrival to locate the vehicles based on communication with a single RSU and uses the vehicle kinematics information obtained via the vehicle's onboard inertial navigation system (INS) to further improve the accuracy of the vehicle location using Kalman filters.
Abstract: Collision avoidance and road safety applications require highly accurate vehicle localization techniques. Unfortunately, the existing localization techniques are not suitable for road safety applications as they rely on the error-prone Global Positioning System (GPS). Likewise, cooperative localization techniques that use intervehicle communications experience high errors due to hidden vehicles and the limited sensing/communication range. Recently, GPS-free localization based on vehicle communication with a low cost infrastructure installed on the roadsides has emerged as a more accurate alternative. However, existing techniques require the vehicle to communicate with two roadside units (RSUs) in order to achieve high localization accuracy. In contrast, this paper presents a GPS-free localization framework that uses two-way time of arrival to locate the vehicles based on communication with a single RSU. Furthermore, our framework uses the vehicle kinematics information obtained via the vehicle's onboard inertial navigation system (INS) to further improve the accuracy of the vehicle location using Kalman filters. Our results show that the localization error of the proposed framework is as low as 1.8 meters. The resulting localization accuracy is up to 65% and 47.5% better than GPS-based techniques used without/with INS, respectively. This accuracy gain becomes around 73.3% when compared to existing RSU-based techniques.

49 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023309
2022657
2021491
2020889
20191,003
20181,013