IMU Self-Calibration Using Factorization
- IMU Self-Calibration Using Factorization
- Myung Hwangbo; 김준식; Takeo Kanade
- Calibration and identification; Factorization
method; Linear algorithm; Inertial measurement
unit; Self-calibration; redundant and triad inertial measurement
- Issue Date
- IEEE transactions on robotics
- VOL 29, NO 2, 493-507
- This paper presents a convenient self-calibration
method for an inertial measurement unit (IMU) using matrix factorization.
Using limited information about applied loads (accelerations
or angular rates) available from natural references, the proposedmethod
can linearly solve all the parameters of anIMUin any
configuration of its inertial components. Our factorization-based
calibration method exploits the bilinear form of an IMU measurement,
which is the product of intrinsic calibration parameters and
exerted loads. For a redundant IMU, we prove that partial knowledge
of the loads, such as magnitude, can produce a linear solution
space for a proper decomposition of the measurement. Theoretical
analysis on this linear space reveals that a 1-D null space should be
considered when load magnitudes are all equal (e.g., gravity loads).
Degenerate load distributions are also geometrically identified to
avoid singular measurement collection. Since a triad IMU has a
lower number of sensor components than a 4-D parameter space,
we propose an iterative factorization in which only initial bias is
required. A wide convergence region of the bias can provide an automatic
setting of the initial bias as the mean of the measurements.
Performance of the proposed method is evaluated with respect to
various noise levels and constraint types. Self-calibration capability
is demonstrated using natural references, which are gravity for
accelerometers and image stream from an attached camera for gyroscopes.
Calibration results are globally optimal and identical to
those of nonlinear optimization.
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