New-Tech Europe Magazine | Sep 2019 | Digital Edition

accelerometer data is therefore filtered out in the short term and smoothed by the gyroscope data. The computational horsepower needed to perform all of this sensor processing, filtering, and fusing consumes energy, which can be a problem in battery-powered systems, especially when the IMU information is not needed as a continuous stream. For many embedded applications, significant power savings can be realized if the IMU can generate an interrupt that awakens the host processor from sleep mode so that it can initiate processing or take some action as a result of the interrupt. To enable this capability, some IMU vendors are starting to incorporate processing and decision making features in their IMUs. Let the IMU do the thinking The 6DOF LSM6DSO from STMicroelectronics is one such IMU. It incorporates three microelectromechanical systems (MEMS) gyroscopes and three MEMS accelerometers and can detect orientation changes and gestures without oversight or assistance from a host processor, all using on board processing. The IMU consumes 0.55 milliamps (mA) running in its highest performance mode. In this mode, the LSM6DSO can continuously monitor its own attitude and movement in space and can generate an interrupt upon a prearranged condition that awakens the host processor to perform additional processing on the sensor stream. Using a low- power IMU that can always remain operational is beneficial because it lets the host processor sleep, awakening it only when necessary.

is possible to measure a device’s heading with respect to magnetic north with high accuracy. Motion tracking using IMUs employs sensor fusion to derive a single, high accuracy estimate of relative device orientation and position from a known starting point and orientation. Sensor fusion usually employs software to combine the IMU’s various motion sensor outputs using complex mathematical algorithms developed either by the IMU manufacturer or the application developer. Position calculations using sensor fusion can produce the following measurements: Gravity – specifically the earth’s gravity, which excludes the acceleration caused by the motion being experienced by the device. An accelerometer measures the gravity vector when the IMU is stationary. When the IMU is in motion, the gravity measurement requires fusing data from an accelerometer and a gyroscope and subtracting out the acceleration caused by motion. Linear acceleration – equivalent to the acceleration of the device as measured by the accelerometer, but with the gravity vector subtracted using software. IMU linear acceleration can be used to measure movement in three- dimensional space. Orientation (attitude) – the set of Euler angles including yaw (azimuth), pitch, and roll, as measured in units of degrees. Rotation vector – derived from a combination of data from accelerometer, gyroscope, and magnetometer sensors. The rotation vector represents a rotation angle around a specified axis. Sources of IMU error Gyroscopes sense orientation

through angular velocity changes, but they tend to drift over time because they only sense changes and have no fixed frame of reference. Adding accelerometer data to the gyroscope data allows software to minimize gyroscope bias for a more accurate location estimate. Accelerometers sense changes in direction with respect to gravity, and that data can be used to orient a gyroscope. Accelerometers are more accurate for static (as opposed to dynamic) calculations. Gyroscopes are better at detecting orientation when the system is already in motion. Accelerometers react quickly, so accelerometer jitter and noise produce accumulated error when that data is used alone. Additionally, accelerometers tend to distort accelerations due to external forces such as gravitational forces, which also accumulate in the system as noise. Filtering this data improves accuracy. Combining a gyroscope’s short-term accuracy with an accelerometer’s long-term accuracy results in more precise orientation readings by relying on each sensor’s strengths to cancel or at least reduce the other sensor’s weaknesses. The two sensor types complement each other to help reduce errors, but there are other ways by which errors are reduced. Fused filtering needed to reduce error IMU software uses filtering to minimize positioning error from IMU data. Several filtering methods for fusing sensor data are available, each with varying degrees of complexity. A complementary filter combines a high pass gyroscope filter and low pass accelerometer filter. High frequency noise in the

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