融合雷达、视觉与惯性数据，实现在黑暗低纹理环境下的稳定定位。
Tightly-Coupled Radar-Visual-Inertial Odometry
- 紧耦合融合图像特征、雷达多普勒与惯性数据，实时估计位姿。
- 在室内外复杂环境下验证，光照缺失或快速飞行时仍保持精度。
- 适合无人机等轻量平台，在视觉失效场景下依然可靠运行。
视觉-惯性里程计（VIO）因其多功能性和良好性能，常用于资源受限的轻量化平台进行状态估计。然而，在黑暗、低纹理或遮挡环境中，其鲁棒性受到挑战。相比之下，调频连续波（FMCW）雷达和雷达-惯性里程计（RIO）虽对这些视觉挑战具有更强鲁棒性，但信息密度较低且长期精度较差。为此，本文提出一种紧耦合的雷达-视觉-惯性里程计方法，通过在迭代扩展卡尔曼滤波器（IEKF）中实时融合图像特征、雷达多普勒测量值和惯性测量单元（IMU）数据，利用雷达距离信息辅助视觉特征深度初始化。该方法在室内外多种环境中进行了飞行实验，涵盖黑暗、雾天及高速飞行等极端条件，全面验证了其鲁棒性。代码已开源：https://github.com/ntnu-arl/radvio。
原文摘要 · Abstract (English)
Visual-Inertial Odometry (VIO) is a staple for reliable state estimation on constrained and lightweight platforms due to its versatility and demonstrated performance. However, pertinent challenges regarding robust operation in dark, low-texture, obscured environments complicate the use of such methods. Alternatively, Frequency Modulated Continuous Wave (FMCW) radars, and by extension Radar-Inertial Odometry (RIO), offer robustness to these visual challenges, albeit at the cost of reduced information density and worse long-term accuracy. To address these limitations, this work combines the two in a tightly coupled manner, enabling the resulting method to operate robustly regardless of environmental conditions or trajectory dynamics. The proposed method fuses image features, radar Doppler measurements, and Inertial Measurement Unit (IMU) measurements within an Iterated Extended Kalman Filter (IEKF) in real-time, with radar range data augmenting the visual feature depth initialization. The method is evaluated through flight experiments conducted in both indoor and outdoor environments, as well as through challenges to both exteroceptive modalities (such as darkness, fog, or fast flight), thoroughly demonstrating its robustness. The implementation of the proposed method is available at: https://github.com/ntnu-arl/radvio.
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