Enhanced Dynamic Obstacle Avoidance for UAVs Using Event Camera and Ego-Motion Compensation
Bibliographic record
Abstract
To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".