A review of SLAM techniques and applications in unmanned aerial vehicles
Bibliographic record
Abstract
Abstract Simultaneous Localisation and Mapping (SLAM) is a foundational idea in the field of robotics. It involves the processing of sensor signals and the optimisation of pose-graphs. SLAM has found several applications in various domains, including but not limited to courier services, agriculture, environmental monitoring, and military operations, particularly with the use of Unmanned Aerial Vehicles (UAVs). There exist several applications. This work aims to provide a comprehensive analysis of three Simultaneous Localization and Mapping (SLAM) algorithms, namely CNN-SLAM, Linearized Kalman Filter (LKF), and Extended Kalman Filter (EKF). Additionally, it will explore the utilisation of SLAM algorithms in Unmanned Aerial Vehicles (UAVs) by examining its use in precision agriculture, geological surveys, and Emergency Scenarios. This section will outline certain issues that SLAM algorithms may encounter in relation to wide area applications, real-time processing and efficiency, robustness, and dynamic objects within the environment. Ultimately, this study will undertake a comparative analysis of the merits and drawbacks associated with the three algorithms, while also putting up potential remedies for the aforementioned issues.
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.
How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".