Real-time 6-DoF pose estimation by an event-based camera using active LED markers

Adam Loch, Gerald Ebmer, Minh Nhat Vu, Germain Haessig, Roberto Mecca, Markus Vincze, Christian Hartl-Nesic, Andreas Kugi

Research output: Chapter in Book or Conference ProceedingsConference Proceedings with Poster Presentationpeer-review

Abstract

Real-time applications for autonomous operations depend largely on fast and robust vision-based localization systems. Since image processing tasks require processing large amounts of data, the computational resources often limit the performance of other processes. To overcome this limitation, traditional marker-based localization systems are widely used since they are easy to integrate and achieve reliable accuracy. However, classical marker-based localization systems significantly depend on standard cameras with low frame rates, which often lack accuracy due to motion blur. In contrast, event-based cameras provide high temporal resolution and a high dynamic range, which can be utilized for fast localization tasks, even under challenging visual conditions. This paper proposes a simple but effective event-based pose estimation system using active LED markers (ALM) for fast and accurate pose estimation. The proposed algorithm is able to operate in real time with a latency below 0.5 ms while maintaining output rates of 3 kHz. Experimental results in static and dynamic scenarios are presented to demonstrate the performance of the proposed approach in terms of computational speed and absolute accuracy, using the OptiTrack system as the basis for measurement. Moreover, we demonstrate the feasibility of the proposed approach by deploying the hardware, i.e., the event-based camera and ALM, and the software in a real quadcopter application. Our project page is available at: almpose.github.io
Original languageEnglish
Title of host publicationProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2024
Pages8137-8146
Number of pages10
Publication statusE-pub ahead of print - 25 Oct 2023

Research Field

  • High-Performance Vision Systems

Keywords

  • Event-based vision
  • Active markers

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