PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features

TL;DR

This paper introduces PL-EVIO, a robust monocular event-based visual-inertial odometry system that combines point, line, and image features to improve accuracy and reliability during high-speed and dynamic scenarios.

Contribution

It presents a novel optimization-based VIO method that effectively integrates event-corner, line, and image features, leveraging their complementary strengths for enhanced performance.

Findings

Outperforms state-of-the-art image-based and event-based VIO methods in benchmarks.

Demonstrates real-time operation on a quadrotor during autonomous flight.

Achieves high accuracy in diverse outdoor and high-speed scenarios.

Abstract

Event cameras are motion-activated sensors that capture pixel-level illumination changes instead of the intensity image with a fixed frame rate. Compared with the standard cameras, it can provide reliable visual perception during high-speed motions and in high dynamic range scenarios. However, event cameras output only a little information or even noise when the relative motion between the camera and the scene is limited, such as in a still state. While standard cameras can provide rich perception information in most scenarios, especially in good lighting conditions. These two cameras are exactly complementary. In this paper, we proposed a robust, high-accurate, and real-time optimization-based monocular event-based visual-inertial odometry (VIO) method with event-corner features, line-based event features, and point-based image features. The proposed method offers to leverage the point-based features in the nature scene and line-based features in the human-made scene to provide more additional structure or constraints information through well-design feature management. Experiments in the public benchmark datasets show that our method can achieve superior performance compared with the state-of-the-art image-based or event-based VIO. Finally, we used our method to demonstrate an onboard closed-loop autonomous quadrotor flight and large-scale outdoor experiments. Videos of the evaluations are presented on our project website: https://b23.tv/OE3QM6j