ESVIO: Event-based Stereo Visual Inertial Odometry

TL;DR

ESVIO introduces a novel stereo visual-inertial odometry system utilizing event cameras, standard images, and inertial data to achieve robust, real-time state estimation in challenging environments like low-light conditions.

Contribution

This work is the first to develop an event-based stereo visual-inertial odometry system, combining event streams, images, and inertial measurements for improved robustness.

Findings

ESVIO outperforms existing image-based and event-based methods on multiple datasets.

Both pure event-based (ESIO) and image-aided (ESVIO) approaches show superior accuracy.

The system is validated in real-world quadrotor flights and large-scale experiments.

Abstract

Event cameras that asynchronously output low-latency event streams provide great opportunities for state estimation under challenging situations. Despite event-based visual odometry having been extensively studied in recent years, most of them are based on monocular and few research on stereo event vision. In this paper, we present ESVIO, the first event-based stereo visual-inertial odometry, which leverages the complementary advantages of event streams, standard images and inertial measurements. Our proposed pipeline achieves temporal tracking and instantaneous matching between consecutive stereo event streams, thereby obtaining robust state estimation. In addition, the motion compensation method is designed to emphasize the edge of scenes by warping each event to reference moments with IMU and ESVIO back-end. We validate that both ESIO (purely event-based) and ESVIO (event with image-aided) have superior performance compared with other image-based and event-based baseline methods on public and self-collected datasets. Furthermore, we use our pipeline to perform onboard quadrotor flights under low-light environments. A real-world large-scale experiment is also conducted to demonstrate long-term effectiveness. We highlight that this work is a real-time, accurate system that is aimed at robust state estimation under challenging environments.