Interchangeable Visual Inertial LiDAR Odometry and Mapping Payload Unit for Aerial Vehicles
Bibliographic record
Abstract
This paper presents an aeronautical-grade payload unit designed for real-time execution of visual-inertial-LiDAR odometry and mapping (VILOAM) algorithms. The payload offers platform interchangeability between full-scale aircraft (e.g., Bell 412 helicopter), small-scale drones (e.g., DJI M600), and ground platforms. The use of small-scale drones renders a convenient option for the research and development of VILOAM algorithms due to the reduced resource demand and simplified pilot training, while full-scale aircraft experiments provide important operationally relevant datasets to test navigation algorithm performance for field deployment. The payload unit consists of two monocular cameras, an inertial measurement unit (IMU), a light detection and ranging (LiDAR) sensor, and a real-time kinematic (RTK) enabled global navigation satellite system (GNSS) receiver. A portable GPU interfaces with these sensors to capture hardware time-synchronized sensing data and perform real-time VILOAM, including support for AI modules for obstacle detection, emergency landing zone detection that typically occurs in field robotic applications such as last-mile goods delivery, surveillance and search and rescue flights. Field validation results for the payload unit are provided by running the developed VILOAM algorithm, as well as state-of-the-art VILOAM algorithms, and evaluating their performance in real-time localization and mapping on both platforms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.002 |
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".