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.

Continuous 3D scan-matching with a spinning 2D laser

Abstract: Scan-matching is a technique that can be used for building accurate maps and estimating vehicle motion by comparing a sequence of point cloud measurements of the environment taken from a moving sensor. One challenge that arises in mapping applications where the sensor motion is fast relative to the measurement time is that scans become locally distorted and difficult to align. This problem is common when using 3D laser range sensors, which typically require more scanning time than their 2D counterparts. Existing 3D mapping solutions either eliminate sensor motion by taking a “stop-and-scan” approach, or attempt to correct the motion in an open-loop fashion using odometric or inertial sensors. We propose a solution to 3D scan-matching in which a continuous 6DOF sensor trajectory is recovered to correct the point cloud alignments, producing locally accurate maps and allowing for a reliable estimate of the vehicle motion. Our method is applied to data collected from a 3D spinning lidar sensor mounted on a skid-steer loader vehicle to produce quality maps of outdoor scenes and estimates of the vehicle trajectory during the mapping sequences.

Using Inertial Sensors for Position and Orientation Estimation

Abstract: In recent years, MEMS inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

Miniaturized inertial measurement unit and associated methods

Abstract: A self-contained, integrated micro-cube-sized inertial measurement unit is provided wherein accuracy is achieved through the use of specifically oriented sensors, the orientation serving to substantially cancel noise and other first-order effects, and the use of a noise-reducing algorithm such as wavelet cascade denoising and an error correcting algorithm such as a Kalman filter embedded in a digital signal processor device. In a particular embodiment, a pair of three sets of angle rate sensors are orientable triaxially in opposite directions, wherein each set is mounted on a different sector of a base orientable normal to the other two and comprising N gyroscopes oriented at 360/N-degree increments, where N ≥ 2. At least one accelerometer is included to provide triaxial data. Signals are output from the angle rate sensors and accelerometer for calculating a change in attitude, position, angular rate, acceleration, and/or velocity of the unit.

A survey of depth and inertial sensor fusion for human action recognition

Abstract: A number of review or survey articles have previously appeared on human action recognition where either vision sensors or inertial sensors are used individually. Considering that each sensor modality has its own limitations, in a number of previously published papers, it has been shown that the fusion of vision and inertial sensor data improves the accuracy of recognition. This survey article provides an overview of the recent investigations where both vision and inertial sensors are used together and simultaneously to perform human action recognition more effectively. The thrust of this survey is on the utilization of depth cameras and inertial sensors as these two types of sensors are cost-effective, commercially available, and more significantly they both provide 3D human action data. An overview of the components necessary to achieve fusion of data from depth and inertial sensors is provided. In addition, a review of the publicly available datasets that include depth and inertial data which are simultaneously captured via depth and inertial sensors is presented.

Inertial Sensor-Based Gait Recognition: A Review

Abstract: With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability.