面向高速无GPS环境的嵌入式热惯性里程计实时系统
Real-Time Thermal-Inertial Odometry on Embedded Hardware for High-Speed GPS-Denied Flight
AI总结：
该研究提出了一种嵌入式实时热惯性里程计系统，通过融合热成像与惯性数据实现高速无GPS环境下可靠飞行
摘要：
我们提出了一种实时单目热惯性里程计系统，专为在嵌入式硬件上实现高速、无GPS飞行而设计。该系统融合了FLIR Boson+ 640长波红外相机、高采样率惯性测量单元（IMU）、激光雷达、气压计和磁力计的测量数据，采用固定滞后因子图进行处理。为在运动模糊、低对比度和快速视角变化下保持可靠的特征轨迹，我们采用轻量级热优化前端和多阶段特征过滤。激光雷达测量为每个特征提供深度先验，稳定了弱观测运动中的尺度。高采样率惯性数据首先使用Chebyshev类型II无限脉冲响应（IIR）滤波器预过滤，然后预积分，提高了在剧烈机动中对飞行器振动的鲁棒性。为解决高速飞行中气压高度误差，我们训练了一个不确定性感知门控循环单元（GRU）网络，模型静态压力畸变的时序动态，优于多项式和多层感知机（MLP）基线。在NVIDIA Jetson Xavier NX上集成的完整系统支持在30 m/s的四旋翼飞行中，千米级轨迹下的漂移低于2%。这些贡献扩展了热惯性导航的操作范围，使在视觉退化和无GPS环境中可靠高速飞行成为可能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
