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.

Method and apparatus for borehole surveying

Abstract: The invention is, in its various aspects, a method and apparatus useful for strapdown inertial navigation and surveying in a borehole. The method comprises maneuvering a probe including three vibrating mass, Coriolis effect gyroscopes in a borehole and initializing the probe's attitude in the borehole within the probe's frame of reference. Three orthogonal, incremental rotation angles and three orthogonal, incremental velocities are determined for the probe within the probe's frame of reference. The incremental rotation angles are determined from the gyroscopes. The method then translates the three incremental velocities from the probe's frame of reference into the inertial frame of reference using the three incremental rotation angles. Next, a velocity vector in a local-vertical, wander-azimuth frame of reference is determined from the translated incremental velocities. A velocity error observation is then obtained. A system error is then estimated from the velocity vector and the velocity error observation. The system error is then fed back into the inertial navigation system for use in refining the method. In as second aspect, the invention is a strapdown, inertial measurement unit. The inertial measurement unit includes a housing, three accelerometers, and three vibrating mass, Coriolis effect gyroscopes. The three accelerometers are mounted within the housing. The three vibrating mass, Coriolis effect are rigidly mounted within the housing.

ETHOS: Miniature orientation sensor for wearable human motion analysis

Abstract: Inertial and magnetic sensors offers a sourceless and mobile option to obtain body posture and motion for personal sports or healthcare assistants, if sensors could be unobtrusively integrated in casual garments and accessories. We present in this paper design, implementation, and evaluation results for a novel miniature attitude and heading reference system (AHRS) named ETHOS using current off-the-shelf technologies. ETHOS has a unit size of 2.5cm3, which is substantially below most currently marketed attitude heading reference systems, while the unit contains processing resources to estimate its orientation online. Results on power consumption in relation to sampling frequency and sensor use are presented. Moreover two sensor fusion algorithms to estimate orientation: a quaternion-based Kalman-, and a complementary filter. Evaluations of orientation estimation accuracy in static and dynamic conditions revealed that complementary filtering reached sufficient accuracy while consuming 46% of a Kalman's power. The system runtime of ETHOS was found to be 10 hours at a complementary filter update rate of 128Hz. Furthermore, we found that a ETHOS prototype functioned with a sufficient accuracy in estimating human movement in real-life conditions using an arm rehabilitation robot.

Video: LookUp!: Enabling Pedestrian Safety Services via Shoe Sensing

Abstract: This video is a demonstration of the work discussed in our full paper available in the MobiSys'15 proceedings. The video illustrates a sensing technology for fine-grained location classification in an urban environment, for enhancing pedestrian safety. Our system seeks to detect the transitions from sidewalk locations to in-street locations, to enable applications such as alerting texting pedestrians when they step into the street. Existing positioning technologies are not sufficiently precise to allow distinguishing a position on the sidewalk from a position in the street, as explored in our previous work. To this end, we use shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. This approach is different from existing shoe sensing solutions that focus on dead reckoning and inertial navigation. The shoe sensors relay inertial sensor measurements to a smartphone, which extracts the step pattern and the inclination of the ground a pedestrian is walking on. This allows detecting transitions such as stepping over a curb or walking down sidewalk ramps that lead into the street. We carried out walking trials in metropolitan environments in United States (Manhattan) and Europe (Turin). The results from these experiments show that we can accurately determine transitions between sidewalk and street locations to identify pedestrian risk.

Estimation of land vehicle roll and pitch angles

Abstract: In this article, two kinematics-based observers are proposed to estimate the vehicle roll and pitch angles by using an inertial measurement unit. The observers are mathematically proven to be stable if the vehicle yaw rate is not zero. With a design variation of the observer gains, the estimated roll or pitch angle is shown to further asymptotically converge to the true value, eliminating possible errors caused by the biases of the acceleration signals. Simulation results show that accurate estimation of both pitch and roll angles can be achieved without the help of external sensors such as global positioning systems, either by using the accelerometer-based reference pitch or roll angle as the maneuver varies, or by using an observer with zero steady-state error property.

Inertial Navigation Aiding by Stationary Updates

Abstract: Sensor-aided inertial navigation has successfully been used for decades for localization of a roving body. When the rover is known to be stationary, artificial “stationary” measurements (i.e., zero velocity and/or zero angular rate) may be imposed. This corrects the velocity, attitude, and inertial measurement unit (IMU) biases, which decreases the rate of drift of the position and attitude. Implementation requires reliable automated tests to detect periods when the vehicle is stationary. Due to cost concerns, methods that use sensors that are already on the vehicle are preferred. This paper reviews existing stationary detection methods and proposes a new frequency domain approach, using only IMU data, to detect stationarity, with specifications and analysis for land vehicles. The performance of this new approach is evaluated in both theory and practice. In addition, this paper presents analytic and numeric evaluations of the observability of the inertial navigation system (INS) error states with stationary updates. Improvements in localization performance in an INS with stationary detection and aiding is shown experimentally.