Asynchronous event feature generation and tracking based on gradient descriptor for event cameras. (7th July 2021)

• Record Type: Journal Article
• Authors: Li, Ruoxiang; Shi, Dianxi; Zhang, Yongjun; Li, Ruihao; Wang, Mingkun
• Is Part Of: International journal of advanced robotic systems. Volume 18:Number 4(2021)
• Publication Date: 2021-07-07
• Subjects: Robotics -- event camera -- feature descriptor -- feature tracking -- SLAM; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298814211027028
• Languages: English
• ISSNs: 1729-8806

Abstract

Recently, the event camera has become a popular and promising vision sensor in the research of simultaneous localization and mapping and computer vision owing to its advantages: low latency, high dynamic range, and high temporal resolution. As a basic part of the feature-based SLAM system, the feature tracking method using event cameras is still an open question. In this article, we present a novel asynchronous event feature generation and tracking algorithm operating directly on event-streams to fully utilize the natural asynchronism of event cameras. The proposed algorithm consists of an event-corner detection unit, a descriptor construction unit, and an event feature tracking unit. The event-corner detection unit addresses a fast and asynchronous corner detector to extract event-corners from event-streams. For the descriptor construction unit, we propose a novel asynchronous gradient descriptor inspired by the scale-invariant feature transform descriptor, which helps to achieve quantitative measurement of similarity between event feature pairs. The construction of the gradient descriptor can be decomposed into three stages: speed-invariant time surface maintenance and extraction, principal orientation calculation, and descriptor generation. The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking. We implement the proposed algorithm in C++ and evaluate it on a public event dataset. The experimental results show that our proposed method achieves improvement in terms of tracking accuracy and real-time performance when compared with the state-of-the-art asynchronous event-corner tracker and with no compromise on the feature tracking lifetime.