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 .

翻译：视觉-惯性里程计（VIO）因其通用性和已证实的性能，成为受约束轻量化平台上可靠状态估计的主流方法。然而，在黑暗、低纹理、遮挡等复杂环境中稳健运行的相关挑战，限制了此类方法的应用。与之相比，调频连续波（FMCW）雷达及其衍生的雷达-惯性里程计（RIO）对此类视觉挑战具有鲁棒性，但代价是信息密度降低和长期精度下降。针对这些局限性，本文以紧耦合方式融合两种方法，使所提方法能在任意环境条件或轨迹动态下稳健运行。该方法在迭代扩展卡尔曼滤波器（IEKF）框架内实时融合图像特征、雷达多普勒测量和惯性测量单元（IMU）数据，并利用雷达测距信息增强视觉特征深度初始化。通过在室内外环境以及干扰两种外部感知模态（如黑暗、雾气和快速飞行）的飞行实验中对方法进行评估，充分验证其鲁棒性。所提方法的实现代码见：https://github.com/ntnu-arl/radvio。