Estimates accelerometer and gyroscope biases from data recorded during a static (motionless) period. The accelerometer bias is computed relative to the expected gravity vector, and the gyroscope bias is the mean angular velocity during the static period.
Value
A list with components:
- accel_bias
Numeric vector of length 3. Mean accelerometer bias for each axis. For a perfectly calibrated sensor at rest, this would be (0, 0, 0) after accounting for gravity.
- gyro_bias
Numeric vector of length 3. Mean gyroscope bias for each axis (rad/s). A stationary sensor should read zero; any offset is the bias.
Details
The function assumes the sensor is stationary. The accelerometer bias is estimated by subtracting the expected gravity contribution from the mean accelerometer reading. Gravity direction is inferred from the mean acceleration direction, and its expected magnitude is 9.81 m/s^2.
The gyroscope bias is simply the mean of the gyroscope readings during the static period (should be near zero for a stationary sensor).
References
Madgwick SOH, Harrison AJL, Vaidyanathan R (2011). "Estimation of IMU and MARG orientation using a gradient descent algorithm." IEEE International Conference on Rehabilitation Robotics.
Examples
# Simulate static IMU data with known biases
n <- 500
accel_bias_true <- c(0.05, -0.03, 0.02)
gyro_bias_true <- c(0.001, -0.002, 0.0015)
accel <- matrix(rep(c(0, 0, -9.81), each = n), ncol = 3) +
matrix(rep(accel_bias_true, each = n), ncol = 3)
gyro <- matrix(rep(gyro_bias_true, each = n), ncol = 3)
cal <- calibrateIMU(accel, gyro)