  This paper presents a novel filter with low computational demand to address
the problem of orientation estimation of a robotic platform. This is
conventionally addressed by extended Kalman filtering of measurements from a
sensor suit which mainly includes accelerometers, gyroscopes, and a digital
compass. Low cost robotic platforms demand simpler and computationally more
efficient methods to address this filtering problem. Hence nonlinear observers
with constant gains have emerged to assume this role. The nonlinear
complementary filter is a popular choice in this domain which does not require
covariance matrix propagation and associated computational overhead in its
filtering algorithm. However, the gain tuning procedure of the complementary
filter is not optimal, where it is often hand picked by trial and error. This
process is counter intuitive to system noise based tuning capability offered by
a stochastic filter like the Kalman filter. This paper proposes the right
invariant formulation of the complementary filter, which preserves Kalman like
system noise based gain tuning capability for the filter. The resulting filter
exhibits efficient operation in elementary embedded hardware, intuitive system
noise based gain tuning capability and accurate attitude estimation. The
performance of the filter is validated using numerical simulations and by
experimentally implementing the filter on an ARDrone 2.0 micro aerial vehicle
platform.
