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.

Real-Time Sampling-based Online Planning for Drone Interception

This paper studies high-speed online planning in dynamic environments. The problem requires finding time-optimal trajectories that conform to system dynamics, meeting computational constraints for real-time adaptation, and accounting for uncertainty from environmental changes. To address these challenges, we propose a sampling-based online planning algorithm that leverages neural network inference to replace time-consuming nonlinear trajectory optimization, enabling rapid exploration of multiple trajectory options under uncertainty. The proposed method is applied to the drone interception problem, where a defense drone must intercept a target while avoiding collisions and handling imperfect target predictions. The algorithm efficiently generates trajectories toward multiple potential target drone positions in parallel. It then assesses trajectory reachability by comparing traversal times with the target drone's predicted arrival time, ultimately selecting the minimum-time reachable trajectory. Through extensive validation in both simulated and real-world environments, we demonstrate our method's capability for high-rate online planning and its adaptability to unpredictable movements in unstructured settings.

NVINS: Robust Visual Inertial Navigation Fused with NeRF-augmented Camera Pose Regressor and Uncertainty Quantification

In recent years, Neural Radiance Fields (NeRF) have emerged as a powerful tool for 3D reconstruction and novel view synthesis. However, the computational cost of NeRF rendering and degradation in quality due to the presence of artifacts pose significant challenges for its application in real-time and robust robotic tasks, especially on embedded systems. This paper introduces a novel framework that integrates NeRF-derived localization information with Visual-Inertial Odometry(VIO) to provide a robust solution for robotic navigation in a real-time. By training an absolute pose regression network with augmented image data rendered from a NeRF and quantifying its uncertainty, our approach effectively counters positional drift and enhances system reliability. We also establish a mathematically sound foundation for combining visual inertial navigation with camera localization neural networks, considering uncertainty under a Bayesian framework. Experimental validation in the photorealistic simulation environment demonstrates significant improvements in accuracy compared to a conventional VIO approach.