Monocular VO Scale Ambiguity Resolution Using an Ultra Low-Cost Spike Rangefinder
Bibliographic record
Abstract
Monocular visual odometry (VO) is the process of determining a user’s trajectory through a series of consecutive images taken by a single camera. A major problem that affects the accuracy of monocular visual odometry, however, is the scale ambiguity. This research proposes an innovative augmentation technique, which resolves the scale ambiguity problem of monocular visual odometry. The proposed technique augments the camera images with range measurements taken by an ultra-low-cost laser device known as the Spike. The size of the Spike laser rangefinder is small and can be mounted on a smartphone. Two datasets were collected along precisely surveyed tracks, both outdoor and indoor, to assess the effectiveness of the proposed technique. The coordinates of both tracks were determined using a total station to serve as a ground truth. In order to calibrate the smartphone’s camera, seven images of a checkerboard were taken from different positions and angles and then processed using a MATLAB-based camera calibration toolbox. Subsequently, the speeded-up robust features (SURF) method was used for image feature detection and matching. The random sample consensus (RANSAC) algorithm was then used to remove the outliers in the matched points between the sequential images. The relative orientation and translation between the frames were computed and then scaled using the spike measurements in order to obtain the scaled trajectory. Subsequently, the obtained scaled trajectory was used to construct the surrounding scene using the structure from motion (SfM) technique. Finally, both of the computed camera trajectory and the constructed scene were compared with ground truth. It is shown that the proposed technique allows for achieving centimeter-level accuracy in monocular VO scale recovery, which in turn leads to an enhanced mapping accuracy.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".