Bibliographic record
Abstract
Laser mapping has become quite popular in recent days due to its capability of providing information directly in three dimensions. A Terrestrial Laser Scanning (TLS) system operates by emitting and locating returned laser pulses. Locating the returned pulses requires knowing precisely for each laser pulse, the vehicle position (e.g. GPS), the attitude of the vehicle using Inertial Measurement Unit (lMU), the scanner angle when the laser pulse left the sensor, and the slant range to the surface for that pulse. One of the most critical sources of error in TLS or any other laser scanning system is the angular misalignment between the scanner and the IMU, which is called misalignment or boresight error. This error must be addressed before a TLS system can accurately produce data. The purpose of this research is to develop a method and identify the requirements for calculating the small misalignment angles between the laser scanner and the combined GPS/lMU solution for position and attitude. A mathematical model is developed in order to acquire the misalignment angles, using simulated data which consists of coordinates of target points, position of the scanner, rotation matrix of the IMU, and the product matrix (i. e. [LU., l1y, I1z]T) derived from the range and the MATLAB program which initially solves for the Projection Matrix using preset boresight angles (Rb). The equation is then rearranged to solve for the Rb as the goal is to obtain the same prearranged values that are initially used in the first part of the analysis. The calculation of the misalignment angles is considered to be successful as the prearranged Roll, Pitch, and Heading values are obtained after a few iteration, verifying that the mathematical model is sufficient for the purpose of calibrating the Terrestrial Laser Scanner.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".