Exploring Event Camera-based Odometry for Planetary Robots
This work addresses the need for reliable vision-based odometry for future Mars helicopter missions, offering a robust solution to depth uncertainties and motion challenges, though it is incremental as it builds on existing event-based and filter-based techniques.
The paper tackles the problem of high tracking errors and brittleness in event-based visual-inertial odometry (VIO) for planetary robots by introducing EKLT-VIO, which combines an event-based frontend with a filter-based backend, outperforming existing methods by 32% on benchmarks and demonstrating robustness in Mars-like conditions.
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.