论文标题
探索行星机器人的基于事件摄像头的循环仪
Exploring Event Camera-based Odometry for Planetary Robots
论文作者
论文摘要
由于它们对运动模糊和在弱光和高动态范围条件下的高度鲁棒性的韧性，事件摄像头有望成为对未来火星直升机任务的基于视觉探索的传感器。但是，现有的基于事件的视觉惯性探测器（VIO）算法要么患有高跟踪误差，要么是脆弱的，因为它们无法应对由于无法预料的跟踪损失或其他效果而导致的显着深度不确定性。在这项工作中，我们介绍了EKLT-VIO，该工作通过将基于事件的最新前端与基于过滤器的后端相结合来解决这两个限制。这使得不确定性既准确又健壮，在挑战性基准方面优于基于事件和基于框架的VIO算法的32％。此外，我们在悬停的条件（胜过现有事件的方法）以及新近收集的类似火星和高动态范围的序列中表现出准确的性能，而现有的基于框架的方法失败了。在此过程中，我们表明基于事件的VIO是基于视觉的火星探索的前进的道路。
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.
