TEVIO: Thermal-Aided Event-Based Visual-Inertial Odometry for Robust State Estimation in Challenging Environments

Gu Gong, Fuji Hu, Fangyuan Wang, Muhammad Muddassir, Peng Zhou, Lu Li, Qiang Wang, Zhen He, David Navarro-Alarcon

Abstract: Event-based visual odometry (VO) excels in high-dynamic-range scenarios but struggles in extremely low-light or low-contrast conditions, motivating the integration of thermal imaging. This article presents thermal-aided event-based visual-inertial odometry (TEVIO), a multimodal system that fuses thermal imaging, event-based vision, and inertial measurements to address the challenges of visual-inertial odometry in low-light, high-dynamic-range, and low-texture environments. An enhanced time surface map (ETSM) improves feature extraction for high-motion and low-texture scenes. A parallel frequency-varied tracking framework then estimates the pose stably and in high precision. Extensive tests on public event camera datasets and real-world outdoor vehicle experiments show TEVIO’s superior tracking accuracy and robustness compared to state-of-the-art monocular methods like EVIO, enabling reliable pose estimation in conditions where conventional approaches fail. A video demonstration is available at https://youtu.be/RfWYU15WwsU.