Head orientation prediction: delta quaternions versus quaternions

A Simulation Environment for Benchmarking Sensor Fusion-Based Pose Estimators

In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented.

Estimation of Cartesian Space Robot Trajectories Using Unit Quaternion Space

The ability to estimate Cartesian space trajectories that include orientation is of great importance for many practical applications. While it is becoming easier to acquire trajectory data by computer vision methods, data measured by general-purpose vision or depth sensors are often rather noisy. Appropriate smoothing methods are thus needed in order to reconstruct smooth Cartesian space trajectories given noisy measurements. In this paper, we propose an optimality criterion for the problem of the smooth estimation of Cartesian space trajectories that include the end-effector orientation. Based on this criterion, we develop an optimization method for trajectory estimation which takes into account the special properties of the orientation space, which we represent by unit quaternions. The efficiency of the developed approach is discussed and experimental results are presented.

A Multiple Model Approach to Track Head Orientation With Delta Quaternions

Virtual reality and augmented reality environments using helmet-mounted displays create a sense of immersion by closely coupling user head motion to display content. Delays in the presentation of visual information can destroy the sense of presence in the simulation environment when it causes a lag in the display response to user head motion. The effect of display lag can be minimized by predicting head orientation, allowing the system to have sufficient time to counteract the delay. In this paper, anew head orientation prediction technique is proposed that uses a multiple delta quaternion (DQ) extended Kalman filter to track angular head velocity and angular head acceleration. This method is independent of the device used for orientation measurement, relying on quaternion orientation as the only measurement data. A new orientation prediction algorithm is proposed that estimates future head orientation as a function of the current orientation measurement and a predicted change in orientation, using the velocity and acceleration estimates. Extensive experimentation shows that the new method improves head orientation prediction when compared to single filter DQ prediction.

Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing

User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.