Gyroscope-free Inertial Measurement Unit Research Articles

A Class of Inertial Measurement Units for Low-Noise Angular Velocity Estimation

An Inertial Measurement Unit (IMU) architecture estimating angular velocity is introduced. It expands the concept of multi-accelerometers gyroscope-free IMUs (GF-IMUs), taking advantage of their excellent noise performance and resolving their inability to compensate for accelerometers’ bias and their requirement for specific accelerometers placement, which makes GF-IMUs inappropriate for real world applications. It does so by embedding the accelerometers in a low-bandwidth closed-loop configuration with a three-axis gyroscope and by addressing the complete non-linear dynamics. Extensive theoretical analysis provides a complete framework for designing low-noise IMUs including sufficient stability criteria for the non-linear system’s dynamics. Simulation and experimental results support the theoretical analysis and indicate that even a minimal system using the proposed architecture can outperform the gyroscope in noise performance by at most 15dB in some frequency range.

The Correcting Approach of Gyroscope-Free Inertial Navigation Based on the Applicable Topological Map

The accumulated error and noise sensitivity are the two common problems of ordinary inertial sensors. An accurate gyroscope is too expensive, which is not normally applicable in low-cost missions of mobile robots. Since the accelerometers are rather cheaper than similar types of gyroscopes, using redundant accelerometers could be considered as an alternative. This mechanism is called gyroscope-free navigation. The article deals with autonomous mobile robot (AMR) navigation based on gyroscope-free method. In this research, the navigation errors of the gyroscope-free method in long-time missions are demonstrated. To compensate the position error, the aid information of low-cost stereo cameras and a topological map of the workspace are employed in the navigation system. After precise sensor calibration, an amendment algorithm is presented to fuse the measurement of gyroscope-free inertial measurement unit (GFIMU) and stereo camera observations. The advantages and comparisons of vision aid navigation and gyroscope-free navigation of mobile robots will be also discussed. The experimental results show the increasing accuracy in vision-aid navigation of mobile robot.

Self-Calibration of Accelerometer Arrays

A gyroscope-free inertial measurement unit employs solely accelerometers to capture the motion of a body in the form of its linear and angular acceleration as well as its angular velocity. For that, multiple transducers are fixed at distinct locations of the body that together form an accelerometer array. To accurately estimate the motion, the poses of the sensors, i.e., their positions and orientations, must be known precisely. Unfortunately, these parameters are typically hard to assess. Current state-of-the-art calibration methods are able to reconstruct the geometrical sensor configuration based on a set of motion data and corresponding acceleration measurements. However, to impose a reference motion on the sensor array and to capture that motion with the necessary accuracy requires sophisticated laboratory equipment. In this paper, we present a method to estimate the transducer poses using only their own measurements without depending on reference motion data. It is based on an iterative graph optimization that considers both the sensor poses and the motion as target variables. Initially, this results in infinitely many solutions. We reduce the solutions to only one global optimum by explicitly modeling the used triple-axis accelerometers as sensor triads and furthermore taking the temporal dependence of the acceleration samples into account. We compare our method to the conventional calibration using reference data in terms of its estimation accuracy. Furthermore, we analyze the convergence properties of our method by evaluating its tolerance to initial pose deviations. For both, we use synthetic and experimental data recorded on a 3-D rotation table.

Observing Relative Motion With Three Accelerometer Triads

A gyroscope-free inertial measurement unit (GF-IMU) detects the relative motion of a body based on acceleration measurements only. It consists of multiple transducers attached at distinct locations within the body that together form an accelerometer array. In this paper, we employ only three accelerometer triads in order to completely capture the transversal and angular acceleration as well as the angular velocity. By modeling the GF-IMU as a nonlinear control system, we are able to conduct an observability analysis, which shows that this approach is capable of capturing an arbitrary spatial motion. We also show that additional triads only provide redundant information. Based on the control system formulation, we derive the models required to employ a nonlinear Kalman filter as a state observer. As the system description is of a general form they are suitable for any accelerometer array regardless of the number and placement of the transducers. Hence, the presented Kalman filter approach is applicable to all observable GF-IMU configurations. The measurements taken with a prototype on a 3-D rotation table confirm the observability analysis. The evaluations also show that the approach using three accelerometer triads achieves an estimation accuracy comparable with that of arrays employing a higher number of triads.

State Derivation of a 12-Axis Gyroscope-Free Inertial Measurement Unit

The derivation of linear acceleration, angular acceleration, and angular velocity states from a 12-axis gyroscope-free inertial measurement unit that utilizes four 3-axis accelerometer measurements at four distinct locations is reported. Particularly, a new algorithm which derives the angular velocity from its quadratic form and derivative form based on the context-based interacting multiple model is demonstrated. The performance of the system was evaluated under arbitrary 3-dimensional motion.

Design, geometry evaluation, and calibration of a gyroscope-free inertial measurement unit

This work reports on a gyroscope-free inertial measurement unit (GF-IMU) that only comprises linear accelerometers in order to directly measure the transversal acceleration as well as the angular acceleration and velocity. The accuracy of the calculated body motion depends on the geometrical setup and the variance of the employed accelerometers. Therefore, an analytical evaluation of the sensor placement for the propagation of the sensor variance is provided here. The positions and orientations of the sensors within the body frame have to be known precisely in order to calculate the exact inertial movement of the body. With the calibration scheme presented here it is possible to identify these parameters completely even without any previous knowledge. Furthermore, the variance of the parameters can be determined, which can be used to evaluate the performance of the calibration. Using only acceleration sensors it is not possible to determine the direction of a rotation. To overcome this drawback, an Unscented Kalman filter (UKF) is applied to merge the information of the angular acceleration and the angular rate and thus robustly estimate the sign of the angular velocity. Measurements on a 3D-rotation table were carried out to exemplarily demonstrate the accuracy improvements after the calibration. Thereby, the RMS error of the angular rate was reduced by a factor of 2.8.

Sensor Fusion Algorithm and Calibration for a Gyroscope-free IMU

This paper presents a gyroscope-free inertial measurement unit (IMU) that only consists of linear acceleration sensors. Only a simple matrix multiplication has to be performed in order to calculate the complete relative movement of a body. However, the precise positions and orientations of the sensors within the body frame have to be known in order to calculate the exact movement of the body. A simple and effective calibration algorithm developed in this paper can be applied to determine these parameters entirely even without any previous knowledge. Measurements on a 3D-rotation table were carried out to demonstrate the accuracy improvements after the calibration. Thereby, the RMS error of the angular rate was reduced by a factor of 2.8.

A scheme for improving the performance of a gyroscope-free inertial measurement unit

A gyroscope-free inertial measurement unit (GF-IMU) uses only accelerometers to compute specific force and angular rate. It is a low-cost inertial system, but its measurement error diverges at a rate that is an order faster than that of a conventional inertial system equipped with gyroscopes. In this paper, a redundant accelerometer-aided gyroscope-free IMU is designed to achieve a stable and bounded system. In this system, a three-axis accelerometer is used in addition to a set of six accelerometers that is typical in a conventional gyroscope-free IMU. The linear error dynamics of this aided gyroscope-free IMU is derived, and the effects of accelerometer errors are analyzed.