Showing papers on "Inertial measurement unit published in 2011"

Estimation of IMU and MARG orientation using a gradient descent algorithm

Abstract: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.

Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration

Abstract: Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the camera—IMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.

Monocular-SLAM–based navigation for autonomous micro helicopters in GPS-denied environments

Abstract: Autonomous micro aerial vehicles (MAVs) will soon play a major role in tasks such as search and rescue, environment monitoring, surveillance, and inspection. They allow us to easily access environments to which no humans or other vehicles can get access. This reduces the risk for both the people and the environment. For the above applications, it is, however, a requirement that the vehicle is able to navigate without using GPS, or without relying on a preexisting map, or without specific assumptions about the environment. This will allow operations in unstructured, unknown, and GPS-denied environments. We present a novel solution for the task of autonomous navigation of a micro helicopter through a completely unknown environment by using solely a single camera and inertial sensors onboard. Many existing solutions suffer from the problem of drift in the xy plane or from the dependency on a clean GPS signal. The novelty in the here-presented approach is to use a monocular simultaneous localization and mapping (SLAM) framework to stabilize the vehicle in six degrees of freedom. This way, we overcome the problem of both the drift and the GPS dependency. The pose estimated by the visual SLAM algorithm is used in a linear optimal controller that allows us to perform all basic maneuvers such as hovering, set point and trajectory following, vertical takeoff, and landing. All calculations including SLAM and controller are running in real time and online while the helicopter is flying. No offline processing or preprocessing is done. We show real experiments that demonstrate that the vehicle can fly autonomously in an unknown and unstructured environment. To the best of our knowledge, the here-presented work describes the first aerial vehicle that uses onboard monocular vision as a main sensor to navigate through an unknown GPS-denied environment and independently of any external artificial aids. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Detecting inertial effects with airborne matter-wave interferometry

Abstract: Inertial sensors relying on atom interferometry offer a breakthrough advance in a variety of applications, such as inertial navigation, gravimetry or ground- and space-based tests of fundamental physics These instruments require a quiet environment to reach their performance and using them outside the laboratory remains a challenge Here we report the first operation of an airborne matter-wave accelerometer set up aboard a 0g plane and operating during the standard gravity (1g) and microgravity (0g) phases of the flight At 1g, the sensor can detect inertial effects more than 300 times weaker than the typical acceleration fluctuations of the aircraft We describe the improvement of the interferometer sensitivity in 0g, which reaches 2 x 10-4 ms-2 / \surdHz with our current setup We finally discuss the extension of our method to airborne and spaceborne tests of the Universality of free fall with matter waves

Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

Abstract: The fusion of inertial and visual data is widely used to improve an object's pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object's absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial/monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor.

Atomic Sensors – A Review

Abstract: We discuss the basic physics and instrumentation issues related to high-performance physical and inertial sensors based on atomic spectroscopy. Recent work on atomic magnetometers, NMR gyroscopes, and atom interferometers is reviewed, with a focus on precision sensing of electromagnetic and gravitational fields and inertial motion. Atomic sensors have growing relevance to many facets of modern science and technology, from understanding the human brain to enabling precision navigation of moving platforms.

Wind Field Estimation for Small Unmanned Aerial Vehicles

Abstract: This paper describes a method for estimating wind field (wind velocity, rate of change of wind velocity, and wind gradient) for small and mini unmanned aerial vehicles. The approach uses sensors that are already part of a standard autopilot sensor suite (Global Positioning System, inertial measurement unit, airspeed, and magnetometer). The primary motivation is enabling energy harvesting; a secondary motivation is development of a low-cost atmospheric measurement and sampling system. The paper presents an error analysis and discusses the primary contributions to error in the estimated wind field. Results of Monte Carlo simulations compare predicted errors in wind estimates with actual errors and show the effect of using estimated winds for energy harvesting from gusts.

Accurate and reliable soldier and first responder indoor positioning: multisensor systems and cooperative localization

Abstract: A robust, accurate positioning system with seamless outdoor and indoor coverage is a highly needed tool for increasing safety in emergency response and military urban operations. It must be lightweight, small, inexpensive, and power efficient, and still provide meter-level accuracy during extended operations. GPS receivers, inertial sensors, and local radio-based ranging are natural choices for a multisensor positioning system. Inertial navigation with foot-mounted sensors is suitable as the core system in GPS denied environments, since it can yield meter-level accuracies for a few minutes. However, there is still a need for additional supporting sensors to keep the accuracy at acceptable levels during the duration of typical soldier and first responder operations. Suitable aiding sensors are three-axis magnetometers, barometers, imaging sensors, Doppler radars, and ultrasonic sensors. Further more, cooperative positioning, where first responders exchange position and error estimates in conjunction with performing radio based ranging, is deemed a key technology. This article provides a survey on technologies and concepts for high accuracy soldier and first responder positioning systems, with an emphasis on indoor positioning.

Towards Miniaturization of a MEMS-Based Wearable Motion Capture System

Abstract: This paper presents a modular architecture to develop a wearable system for real-time human motion capture. The system is based on a network of smart inertial measurement units (IMUs) distributed on the human body. Each of these modules is provided with a 32-bit RISC microcontroller (MCU) and miniaturized MEMS sensors: three-axis accelerometer, three-axis gyroscopes, and three-axis magnetometer. The MCU collects measurements from the sensors and implement the sensor fusion algorithm, a quaternion-based extended Kalman filter to estimate the attitude and the gyroscope biases. The design of the proposed IMU, in order to overcome the problems of the commercial solution, aims to improve performance and to reduce size and weight. In this way, it can be easily embedded in a tracksuit for total body motion reconstruction with considerable enhancement of the wearability and comfort. Furthermore, the main achievements will be presented with a performance comparison between the proposed IMU and some commercial platforms.

Robust Real-Time Visual Odometry with a Single Camera and an IMU

Abstract: The increasing demand for real-time high-precision Visual Odometry systems as part of navigation and localization tasks has recently been driving research towards more versatile and scalable solutions. In this paper, we present a novel framework for combining the merits of inertial and visual data from a monocular camera to accumulate estimates of local motion incrementally and reliably reconstruct the trajectory traversed. We demonstrate the robustness and efficiency of our methodology in a scenario with challenging camera dynamics, and present a comprehensive evaluation against ground-truth data.

Coarse Alignment of a Ship's Strapdown Inertial Attitude Reference System Using Velocity Loci

Abstract: This paper presents a novel estimation method for fast initial coarse alignment of a ship's strapdown inertial attitude reference system using only inertial measurement unit (IMU) measurements for quasi-static alignment and IMU measurements with GPS aiding for moving-base alignment. Unlike several current techniques, the presented estimation method is effective with any initial attitude error. The estimator is based on the decomposition of the attitude quaternion into separate Earth motion, inertial rate, and alignment quaternions. The alignment quaternion is estimated using a minimum variance fit between loci of body- and navigation-frame velocity vectors using solutions to Wahba's problem. One set of vectors is derived from time integrals of measured vehicle motions, and the second set is derived from Earth motion and GPS data (when moving). For the case of quasi-static alignment, an algebraic expression for the covariance of the attitude estimate as a function of the variance of navigation-frame velocity disturbances is developed. It is shown that, by averaging and interleaving the velocity vectors, the resulting attitude estimate is improved over sequential sampling techniques. It is further shown, for a maneuvering vessel, that a continuous estimate of the attitude error covariance can be generated from the IMU data. This latter feature allows direct initialization of a follow-on fine-alignment stochastic estimator's covariance matrix. Results are presented for quasi-static alignment using inertial sensors only and for full in-motion alignment using navigation-frame GPS velocity and position aiding.

Kalman-filter-based orientation determination using inertial/magnetic sensors: observability analysis and performance evaluation.

Abstract: In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical.

Strap-down Pedestrian Dead-Reckoning system

Abstract: This paper presents a waist-worn Pedestrian Dead Reckoning (PDR) System that requires minimal end-user calibration. The PDR system is based on an Inertial Measurement Unit (IMU) comprising of a tri-axial accelerometer, a tri-axial magnetometer and a tri-axial gyroscope. We propose a novel heading estimation scheme using a quaternion-based extended Kalman filter (EKF) that estimates magnetic disturbances and corrects for them. Accelerometer measurements are used to detect step events and to estimate step lengths. Experimental results show that a relative distance error of about 3% to 8% can be obtained using our methods.

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

Joint angle estimation in rehabilitation with inertial sensors and its integration with Kinect

Abstract: In this paper, we explore the combined use of inertial sensors and the Kinect for applications on rehabilitation robotics and assistive devices. In view of the deficiencies of each individual system, a new method based on Kalman filtering was developed in order to perform online calibration of sensor errors automatically whenever measurements from Kinect are available. The method was evaluated on experiments involving healthy subjects performing multiple DOF tasks.

Data fusion algorithms for multiple inertial measurement units.

Abstract: A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system, the analysis of each filter’s performance focuses on these two parameters. Data was collected in two environments, one where GPS signals are moderately attenuated and another where signals are severely attenuated. Accuracy is shown as a function of architecture and the number of IMUs used.

Sensor Fusion and Calibration of Inertial Sensors, Vision, Ultra-Wideband and GPS

Abstract: The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sen ...

Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors [Emerging Opportunities for Localization and Tracking]

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 determine when sensed magnetic heading is accurate enough to be useful for additional error compensation.

Realtime human motion control with a small number of inertial sensors

Abstract: This paper introduces an approach to performance animation that employs a small number of motion sensors to create an easy-to-use system for an interactive control of a full-body human character. Our key idea is to construct a series of online local dynamic models from a prerecorded motion database and utilize them to construct full-body human motion in a maximum a posteriori framework (MAP). We have demonstrated the effectiveness of our system by controlling a variety of human actions, such as boxing, golf swinging, and table tennis, in real time. Given an appropriate motion capture database, the results are comparable in quality to those obtained from a commercial motion capture system with a full set of motion sensors (e.g., XSens [2009]); however, our performance animation system is far less intrusive and expensive because it requires a small of motion sensors for full body control. We have also evaluated the performance of our system by leave-one-out-experiments and by comparing with two baseline algorithms.

Integrated control system for stability control of yaw, roll and lateral motion of a driving vehicle using an integrated sensing system with pitch information

Abstract: An integrated stability control system using the signals from an integrated sensing system for an automotive vehicle includes a plurality of sensors sensing the dynamic conditions of the vehicle. The sensors include an IMU sensor cluster, a steering angle sensor, wheel speed sensors, any other sensors required by subsystem controls. The signals used in the integrated stability controls include the sensor signals; the roll and pitch attitudes of the vehicle body with respect to the average road surface; the road surface mu estimation; the desired sideslip angle and desired yaw rate from a four-wheel reference vehicle model; the actual vehicle body sideslip angle projected on the moving road plane; and the global attitudes. The demand yaw moment used to counteract the undesired vehicle lateral motions (under-steer or over-steer or excessive side sliding motion) are computed from the above-mentioned variables. The braking control is a slip control whose target slip ratios at selective wheels or wheel are directly generated from the request brake pressures computed from the demand yaw moment.

Bilateral step length estimation using a single inertial measurement unit

Abstract: BackgroundThe estimation of the spatio-temporal gait parameters is of primary importance in both physical activity monitoring and clinical contexts. A method for estimating step length bilaterally, during level walking, using a single inertial measurement unit (IMU) attached to the pelvis is proposed. In contrast to previous studies, based either on a simplified representation of the human gait mechanics or on a general linear regressive model, the proposed method estimates the step length directly from the integration of the acceleration along the direction of progression.MethodsThe IMU was placed at pelvis level fixed to the subject's belt on the right side. The method was validated using measurements from a stereo-photogrammetric system as a gold standard on nine subjects walking ten laps along a closed loop track of about 25 m, varying their speed. For each loop, only the IMU data recorded in a 4 m long portion of the track included in the calibrated volume of the SP system, were used for the analysis. The method takes advantage of the cyclic nature of gait and it requires an accurate determination of the foot contact instances. A combination of a Kalman filter and of an optimally filtered direct and reverse integration applied to the IMU signals formed a single novel method (Kalman and Optimally filtered Step length Estimation - KOSE method). A correction of the IMU displacement due to the pelvic rotation occurring in gait was implemented to estimate the step length and the traversed distance.ResultsThe step length was estimated for all subjects with less than 3% error. Traversed distance was assessed with less than 2% error.ConclusionsThe proposed method provided estimates of step length and traversed distance more accurate than any other method applied to measurements obtained from a single IMU that can be found in the literature. In healthy subjects, it is reasonable to expect that, errors in traversed distance estimation during daily monitoring activity would be of the same order of magnitude of those presented.

A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

Abstract: Inertial navigation systems (INS) are composed of inertial sensors, such as accelerometers and gyroscopes. An INS updates its orientation and position automatically; it has an acceptable stability over the short term, however this stability deteriorates over time. Odometry, used to estimate the position of a mobile robot, employs encoders attached to the robot’s wheels. However, errors occur caused by the integrative nature of the rotating speed and the slippage between the wheel and the ground. In this paper, we discuss mobile robot position estimation without using external signals in indoor environments. In order to achieve optimal solutions, a Kalman filter that estimates the orientation and velocity of mobile robots has been designed. The proposed system combines INS and odometry and delivers more accurate position information than standalone odometry.

A Nonlinear Filtering Approach for the Attitude and Dynamic Body Acceleration Estimation Based on Inertial and Magnetic Sensors: Bio-Logging Application

Abstract: This paper addresses the problem of rigid body orientation and Dynamic Body Acceleration (DBA) estimation This work is applied in bio-logging, an interdisciplinary research area at the intersection of animal behavior and bioengineering The proposed approach combines a quaternion-based nonlinear filter with the Levenberg Marquardt Algorithm (LMA) The algorithm has a complementary structure design that exploits measurements from a three-axis accelerometer, a three-axis magnetometer, and a three-axis gyroscope Attitude information is necessary to calculate the animal's DBA in order to evaluate its energy expenditure Some numerical simulations illustrate the nonlinear filter performance Some quantitative assessments prove this efficiency such as the time constant of the filter ( ) and the rms magnitude of the quaternion error ( ) Moreover, the effectiveness of the algorithm is experimentally demonstrated In the experiments a domestic animal is equipped with an Inertial Measurement Unit (MTi-G), which provides a truth attitude for comparison with the complementary nonlinear filter The rms difference between the filter and MTi-G outputs in the free movement experiments is within 0392 rms on roll, 0577 rms on pitch, and 2521 rms on yaw

Decentralised cooperative localisation for heterogeneous teams of mobile robots

Abstract: This paper presents a distributed algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Inter-robot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable. In our proposed formulation, high frequency egocentric data (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server is easily duplicated for full redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

Relative Navigation Between Two Spacecraft Using X-ray Pulsars

Abstract: This paper suggests utilizing X-ray pulsars for relative navigation between two spacecraft in deep space. Mathematical models describing X-ray pulsar signals are presented. The pulse delay estimation problem is formulated, and the Cramer-Rao lower bound (CRLB) for estimation of the pulse delay is given. Two different pulse delay estimators are introduced, and their asymptotic performance is studied. Numerical complexity of each delay estimator, and the effect of absolute velocity errors on its performance is investigated. Using the pulsar measurements, a recursive algorithm is proposed for relative navigation between two spacecraft. The spacecraft acceleration data are provided by the inertial measurement units (IMUs). The pulse delay estimates are used as measurements, and based on models of the spacecraft and IMU dynamics, a Kalman filter is employed to obtain the 3-D relative position and velocity. Furthermore, it is shown that the relative accelerometer biases as well as the differential time between clocks can be estimated. Numerical simulations are also performed to assess the proposed navigation algorithm.

Rapid Fine Strapdown INS Alignment Method under Marine Mooring Condition

Abstract: In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.

Model predictive quadrotor indoor position control

Abstract: This article addresses the control problem of quadrotors in environments where absolute-localization data (GPS, positioning from external cameras) is inadequate. Based on an attached IMU and an optical flow sensor the quadrotor's translational velocity is estimated using an Extended Kalman Filter. Subject to the velocity measurements, the roll, pitch and yaw (RPY) angles, the angular rates and the translational accelerations a switching Model Predictive Controller is designed. The quadrotor dynamics is linearized at various operating points according to the angular rates and the RP-angles. The switching is inferred according to the various linearized models of the quadrotor. The controller is applied on a quadrotor prototype in low-altitude position hold maneuvers at very constrained environments. The experimental results indicate the overall system's efficiency in position/altitude set-point maneuvers.

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Abstract: Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.

Fiber-optic gyroscopes: from design to production [Invited]

Abstract: Fiber-optic gyroscopes (FOGs) represent an important development in the field of inertial sensors and are now considered an alternative technology to mechanical and ring laser gyroscopes for inertial navigation and control applications. The past 30 years of research and development around the world have established the FOG as a critical sensor for high-accuracy inertial navigation systems. In this paper, specifications, system configurations, and error and sensitivity analysis for different types of FOGs, including critical technology, are presented. This paper addresses the design and production of a different FOG that fulfills requirements such as small size, low production cost, low power consumption, and a broad spectrum of applications.