  Due to their resilience to motion blur and high robustness in low-light and
high dynamic range conditions, event cameras are poised to become enabling
sensors for vision-based exploration on future Mars helicopter missions.
However, existing event-based visual-inertial odometry (VIO) algorithms either
suffer from high tracking errors or are brittle, since they cannot cope with
significant depth uncertainties caused by an unforeseen loss of tracking or
other effects. In this work, we introduce EKLT-VIO, which addresses both
limitations by combining a state-of-the-art event-based frontend with a
filter-based backend. This makes it both accurate and robust to uncertainties,
outperforming event- and frame-based VIO algorithms on challenging benchmarks
by 32%. In addition, we demonstrate accurate performance in hover-like
conditions (outperforming existing event-based methods) as well as high
robustness in newly collected Mars-like and high-dynamic-range sequences, where
existing frame-based methods fail. In doing so, we show that event-based VIO is
the way forward for vision-based exploration on Mars.
