A Mobile Robotic Application of Naive Multi-directional Stitching with SIFT
Bibliographic record
Abstract
Generally, any mobile robot must first understand its surroundings before being able to traverse an unknown environment and carry out its intended tasks. To do this, oftentimes a host of sensors are used to detect the terrain around grounded robots, and this data is used to model the same environment virtually. Both Lidar and vision-based sensors, which are very common across most industries, often return points of interest in the form of a point cloud map. Although point cloud data can be an invaluable input for control applications such as path planning and traj ectory tracking, it can sometimes be ambiguous or unhelpful to humans, and costly to compute. To deal with this issue, this paper presents a stitching approach that foregoes the motion model and attempts to create large mosaics of perspective-transformed images from a camera on a mobile robot, utilizing only refined image registration and rejection criteria. To run and validate the proposed stitching algorithm, a hardware test platform was set up, which consists of a battery-powered mobile robot, a mounted stereo vision sensor, and a base PC with the help of ROS.
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) | 0.000 | 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 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".