Interchangeable Visual Inertial LiDAR Odometry and Mapping Payload Unit for Aerial Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it