AERO-VIS: Asynchronous Event-based Real-time Onboard Visual-Inertial SLAM

AIPR assessment

Problem difficulty is high: event-based SLAM on UAVs is a saturated, technically demanding area with strong prior systems and tight real-time constraints. The strengths reinforce each other, since the new event representation, the lighter network, the asynchronous pipeline, and the real-world deployment all point to the same practical thesis. The main weaknesses also interact, since the novelty is mostly incremental and the gains depend on careful system engineering, but the extensive hardware v

Abstract

The robustness of event cameras to high dynamic range and motion blur holds the potential to improve visual odometry systems in challenging environments. Although their high temporal resolution does not require synchronous processing, most event-based odometry methods still run at fixed rates, which simplifies system design but restricts latency and throughput. In this work, we present AERO-VIS, a stereo event-inertial SLAM system with an integrated, data-driven, robust, and performance-optimized keypoint detector. By processing the event stream asynchronously, the system dynamically adapts to downstream runtime demands, ensuring low-latency and real-time performance. When deploying AERO-VIS on a UAV, we achieve unprecedented accuracy in onboard event-based SLAM. These unique characteristics enable us to present the first purely event-based inertial SLAM system that demonstrates closed-loop UAV control and large-scale state estimation while relying solely on onboard compute. A video of the experiments and the source code are available at ethz-mrl.github.io/AERO-VIS.

Score Breakdown

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