A Lightweight RK4-Based Wheel Odometry Integration for OpenVINS
12th International Conference on Control, Decision and Information Technologies, CoDIT 2026, Bari, İtalya, 13 - 16 Temmuz 2026, ss.1698-1703, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/codit70676.2026.11631190
- Basıldığı Şehir: Bari
- Basıldığı Ülke: İtalya
- Sayfa Sayıları: ss.1698-1703
- Hacettepe Üniversitesi Adresli: Evet
Özet
This paper presents a real-world validation of the OpenVINS visual-inertial odometry framework on a custom wheeled mobile robot equipped with a stereo camera, an IMU, and wheel encoders. We additionally introduce a lightweight wheel-odometry integration method that preserves the original OpenVINS MSCKF architecture. Inspired by the RK4-based motion integration strategy used in MINS, the method converts wheel measurements into relative-motion constraints and incorporates them during the filter update without augmenting the estimator state. This design retains the computational profile of standard OpenVINS while providing complementary motion information when visual tracking degrades because of motion blur, low texture, repetitive structure, or illumination changes. Experiments on representative closed-loop trajectories show that the wheel-assisted configuration yields more stable estimates and reduced drift relative to baseline OpenVINS, with the clearest gains in visually challenging sequences. The results indicate that lightweight wheel assistance can improve the practical robustness of OpenVINS for deployable ground robots.