Abstract : This paper introduces a state estimation framework
that allows estimating the attitude, full metric speed and
the orthogonal metric distance of an IMU-camera system with
respect to a plane. The filter relies only on a single optical flow
feature as well as gyroscope and accelerometer measurements.
The underlying assumption is that the observed visual feature
lies on a static plane. The orientation of the observed plane
is not required to be known a priori and is also estimated at
runtime. The estimation framework fuses visual and inertial
measurements in an Unscented Kalman Filter (UKF). The
theoretical limitations of the UKF are investigated using a
nonlinear observability analysis based on Lie-derivatives.
Experiments in simulation using realistic sensor noise values
successfully demonstrate the performance of the filter as well
as validate the findings of the observability analysis. It is shown
that the state estimate is converging correctly, even in presence
of substantial initial state errors. To the authors’ knowledge,
this paper documents for the first time the estimation of the
heading and metric distance to a wall with no range- or bearing
sensors, relying solely on optical flow as the only exteroceptive
sensing modality. This minimal sensor set, that is both lightweight
and low-cost, renders the framework an appealing choice
for the use as a navigation system on a wide range of robotic
platforms, such as ground- or flying robots.