SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation

-- Autonomous flight in GPS-denied indoor spaces requires trajectories that keep visual-localization error tightly bounded across varied missions. Whereas visual inertial odometry (VIO) accumulates drift over time, scene coordinate regression (SCR) yields drift-free, high-accuracy absolute pose estimation. We present a perception-aware framework that couples an evidential learning-based SCR pose estimator with a receding horizon trajectory optimizer . The optimizer steers the onboard camera toward pixels whose uncertainty predicts reliable scene coordinates, while a fixed-lag smoother fuses the low-rate SCR stream with high-rate IMU data to close the perception-control loop in real time. In simulation, our planner reduces translation (rotation) mean error by 54% / 15% (40% / 31%) relative to yaw-fixed and forward-looking baselines, respectively. Moreover, hardware-in-the-loop experiment validates the feasibility of our proposed framework.