Inertial measurement unit

About: Inertial measurement unit is a research topic. Over the lifetime, 13326 publications have been published within this topic receiving 189083 citations. The topic is also known as: IMU.

Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time

Abstract: We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330 000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes.1

Heterogeneous Data Fusion Algorithm for Pedestrian Navigation via Foot-Mounted Inertial Measurement Unit and Complementary Filter

Abstract: This paper proposes a foot-mounted Zero Velocity Update (ZVU) aided Inertial Measurement Unit (IMU) filtering algorithm for pedestrian tracking in indoor environment The algorithm outputs are the foot kinematic parameters, which include foot orientation, position, velocity, acceleration, and gait phase The foot motion filtering algorithm incorporates methods for orientation estimation, gait detection, and position estimation A novel Complementary Filter (CF) is introduced to better pre-process the sensor data from a foot-mounted IMU containing tri-axial angular rate sensors, accelerometers, and magnetometers and to estimate the foot orientation without resorting to GPS data A gait detection is accomplished using a simple states detector that transitions between states based on acceleration and angular rate measurements Once foot orientation is computed, position estimates are obtained by using integrating acceleration and velocity data, which has been corrected at step stance phase for drift using an implemented ZVU algorithm, leading to a position accuracy improvementWe illustrate our findings experimentally by using of a commercial IMU during regular human walking trials in a typical public building Experiment results show that the positioning approach achieves approximately a position accuracy around 04% and improves the performance regarding recent works of literature

Map-Based Precision Vehicle Localization in Urban Environments

Abstract: Many urban navigation applications (eg, autonomous navigation, driver assistance systems) can benefit greatly from localization with centimeter accuracy Yet such accuracy cannot be achieved reliably with GPS-based inertial guidance systems, specifically in urban settings We propose a technique for high-accuracy localization of moving vehicles that utilizes maps of urban environments Our approach integrates GPS, IMU, wheel odometry, and LIDAR data acquired by an instrumented vehicle, to generate high-resolution environment maps Offline relaxation techniques similar to recent SLAM methods [2, 10, 13, 14, 21, 30] are employed to bring the map into alignment at intersections and other regions of self-overlap By reducing the final map to the flat road surface, imprints of other vehicles are removed The result is a 2-D surface image of ground reflectivity in the infrared spectrum with 5cm pixel resolution To localize a moving vehicle relative to these maps, we present a particle filter method for correlating LIDAR measurements with this map As we show by experimentation, the resulting relative accuracies exceed that of conventional GPS-IMU-odometry-based methods by more than an order of magnitude Specifically, we show that our algorithm is effective in urban environments, achieving reliable real-time localization with accuracy in the 10- centimeter range Experimental results are provided for localization in GPS-denied environments, during bad weather, and in dense traffic The proposed approach has been used successfully for steering a car through narrow, dynamic urban roads

VR-STEP: Walking-in-Place using Inertial Sensing for Hands Free Navigation in Mobile VR Environments

Abstract: Low-cost smartphone adapters can bring virtual reality to the masses, but input is typically limited to using head tracking, which makes it difficult to perform complex tasks like navigation Walking-in-place (WIP) offers a natural and immersive form of virtual locomotion that can reduce simulation sickness WIP, however, is difficult to implement in mobile contexts as it typically relies on bulky controllers or an external camera We present VR-STEP; a WIP implementation that uses real-time pedometry to implement virtual locomotion VR-STEP requires no additional instrumentation outside of a smartphone's inertial sensors A user study with 18 users compares VR-STEP with a commonly used auto-walk navigation method and finds no significant difference in performance or reliability, though VR-STEP was found to be more immersive and intuitive

Introduction to Avionics Systems

Abstract: Foreword. Preface. Acknowledgements. 1: Introduction. 1.1. Importance and role of avionics. 1.2. The avionic environment. 1.3. Choice of units. 2: Displays and man-machine interaction. 2.1. Introduction. 2.2. aHead up displays. 2.3. Helmet mounted displays. 2.4. Computer aided optical design. 2.5. Discussion of HUDs vs HMDs. 2.6. Head down displays. 2.7. Data fusion. 2.8. Intelligent displays management. 2.9. Displays technology. 2.10. Control and data entry. Further reading. 3: Aerodynamics and aircraft control. 3.1. Introduction. 3.2. aBasic aerodynamics. 3.3. Aircraft stability. 3.4. Aircraft dynamics. 3.5. Longitudinal control and response. 3.6. Lateral control. 3.7. Powered flying controls. 3.8. Auto-stabilisation systems. Further reading. 4: Fly-by-wire flight control. 4.1. Introduction. 4.2. aFly-by-wire flight control features and advantages. 4.3. Control laws. 4.4. Redundancy and failure survival. 4.5. Digital implementation. 4.6. Fly-by-light flight control. Further reading. 5: Inertial sensors and attitude derivation. 5.1. Introduction. 5.2. Gyros and accelerometers. 5.3. Attitude derivation. Further reading. 6: Navigation systems. 6.1. Introduction and basic principles. 6.2. Inertial navigation. 6.3. Aided IN systems and Kalman filters. 6.4. Attitude and heading reference systems. 6.5. GPS - global positioning systems. 6.6. Terrain reference navigation. Further reading. 7: Air data and air data systems. 7.1. Introduction. 7.2. Air data information and its use. 7.3. Derivation of air data laws and relationships. 7.4. Air data sensors and computing. Further reading. 8: Autopilots and flight management systems. 8.1. Introduction. 8.2. Autopilots. 8.3. Flight management systems. Further reading. 9: Avionic systems integration. 9.1. Introduction and background. 9.2. Data bus systems. 9.3. Integrated modular avionics. 9.4. Commercial off-the-shelf (COTS). Further reading. 10: Unmanned air vehicles. 10.1. Importance of unmanned air vehicles. 10.2. UAV avionics. Further reading. Glossary of terms. List of symbols. List of abbreviations. Index.