aiqbench analysis
This research advances autonomous flight by enabling micro aerial vehicles to navigate at high speeds using only a single camera, eliminating the need for expensive depth sensors like LiDAR. The core innovation lies in a cross-modal distillation technique that transfers geometric planning features from a depth-based expert model to an RGB-only planner, allowing the system to learn collision avoidance without explicit 3D reconstruction. By leveraging simulated data with 3D Gaussian Splatting, the approach achieves robust real-world deployment without requiring further fine-tuning on physical hardware.
Learning Geometry-Aware Planning Representations for Monocular Flight with Cross-Modal Supervision
arXiv:2610.26508v1 Announce Type: new Abstract: Existing high-speed navigation systems for micro aerial vehicles (MAVs) commonly rely on depth cameras, stereo vision, or LiDAR to obtain explicit geometric information, while monocular cameras offer a lighter and lower-cost alternative. However, monocular flight remains challenging because RGB images offer no direct metric cues and visual appearance varies considerably across environments. We propose a geometry-aware monocular visual planning framework that predicts trajectories from two image frames, their relative pose, and the MAV state. Without explicitly reconstructing depth, the proposed method complements the downstream planning objective with feature-level geometric supervision by transferring the planning-relevant representation learned by a privileged depth expert to the RGB planner through cross-modal distillation. The framework first trains a depth-based geometric expert, then distills its planning features into a monocular branch using paired RGB-D observations, and finally adapts the planner to the downstream trajectory planning objective. This design encourages the policy to learn planning-relevant geometric features, enabling collision avoidance. We further use a 3D Gaussian Splatting (3DGS) simulator to render visual observations for data collection, paired RGB-D supervision, and simulation evaluation. The planner trained in simulation is then deployed in real-world environments without real-world fine-tuning, achieving obstacle avoidance at speeds of up to 6.5 m/s.
Abstract:Existing high-speed navigation systems for micro aerial vehicles (MAVs) commonly rely on depth cameras, stereo vision, or LiDAR to obtain explicit geometric information, while monocular cameras offer a lighter and lower-cost alternative. However, monocular flight remains challenging because RGB images offer no direct metric cues and visual appearance varies considerably across environments. We propose a geometry-aware monocular visual planning framework that predicts trajectories from two image frames, their relative pose, and the MAV state. Without explicitly reconstructing depth, the proposed method complements the downstream planning objective with feature-level geometric supervision by transferring the planning-relevant representation learned by a privileged depth expert to the RGB planner through cross-modal distillation. The framework first trains a depth-based geometric expert, then distills its planning features into a monocular branch using paired RGB-D observations, and finally adapts the planner to the downstream trajectory planning objective. This design encourages the policy to learn planning-relevant geometric features, enabling collision avoidance. We further use a 3D Gaussian Splatting (3DGS) simulator to render visual observations for data collection, paired RGB-D supervision, and simulation evaluation. The planner trained in simulation is then deployed in real-world environments without real-world fine-tuning, achieving obstacle avoidance at speeds of up to 6.5 m/s.
Robotics (cs.RO)
arXiv:2610.26508 [cs.RO]
(or arXiv:2610.26508v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.26508 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 292 words