We present a real-time monocular thermal-inertial odometry system designed for high-velocity, GPS-denied flight on embedded hardware. The system fuses measurements from a FLIR Boson+ 640 longwave infrared camera, a high-rate IMU, a laser range finder, a barometer, and a magnetometer within a fixed-lag factor graph. To sustain reliable feature tracks under motion blur, low contrast, and rapid viewpoint changes, we employ a lightweight thermal-optimized front-end with multi-stage feature filtering. Laser range finder measurements provide per-feature depth priors that stabilize scale during weakly observable motion. High-rate inertial data is first pre-filtered using a Chebyshev Type II infinite impulse response (IIR) filter and then preintegrated, improving robustness to airframe vibrations during aggressive maneuvers. To address barometric altitude errors induced at high airspeeds, we train an uncertainty-aware gated recurrent unit (GRU) network that models the temporal dynamics of static pressure distortion, outperforming polynomial and multi-layer perceptron (MLP) baselines. Integrated on an NVIDIA Jetson Xavier NX, the complete system supports closed-loop quadrotor flight at 30 m/s with drift under 2% over kilometer-scale trajectories. These contributions expand the operational envelope of thermal-inertial navigation, enabling reliable high-speed flight in visually degraded and GPS-denied environments.

翻译：本文提出一种面向嵌入式硬件、适用于高速GPS拒止飞行场景的实时单目热惯性里程计系统。该系统在固定滞后因子图中融合了FLIR Boson+ 640长波红外相机、高频率IMU、激光测距仪、气压计与磁力计的测量数据。为在运动模糊、低对比度及视角快速变化条件下维持可靠的特征跟踪，我们采用配备多级特征滤波的轻量化热成像优化前端。激光测距仪测量值提供逐特征深度先验，在弱可观测运动期间稳定尺度估计。高频率惯性数据首先通过切比雪夫II型无限脉冲响应滤波器进行预滤波，随后进行预积分处理，提升了在剧烈机动时机身振动干扰下的鲁棒性。针对高速飞行引起的气压高度误差，我们训练了能够建模静压畸变时序动态特性的不确定性感知门控循环单元网络，其性能优于多项式与多层感知机基线模型。该系统集成于NVIDIA Jetson Xavier NX平台，完整支持30米/秒速度下的四旋翼闭环飞行，在千米级航迹中漂移率低于2%。这些成果拓展了热惯性导航系统的运行边界，为视觉退化与GPS拒止环境下的可靠高速飞行提供了技术支撑。