Boresight and Lever Arm Calibration of a Mobile Terrestrial LiDAR System
Bibliographic record
Abstract
Unlike Mobile Airborne LiDAR (MAL), it has become common for Mobile Terrestrial LiDAR (MTL) sys tems to consist of two or more LiDAR sensors. It is a challenging task for a user to simultaneously verify and calibrate their lever arms and boresight angles with respect to the IMU using the kinematic data. This paper presents a novel method for determination of MTL calibration parameters using the vector geometry created by a stereo pair of MTL sensors. Through the use of the stereo information provided by a pair of MTL sensors working in tandem, system parameters such as lever arm and boresight angles can be deter mined for both sen sors based on a single pass of a calibration object or scene. In this way, any data collected by a multi-sensor MTL can potentially be used to calibrate the system. Unlike many other calibration methods for calibrating MTL and MAL systems, the proposed method enables the simultaneous calibration of all lever arm and bore sight parameters for all of the LiDAR sensors integrated into the MTL system. Many MTL systems do not make it easy for end users to measure the lever arms, usually forcing users to fall back on mechanical drawings to determine the lever arms. The calibration method has been realized using test data acquired by two inde pend ent Lynx Mobile Mapper systems on 1) a single pass of a typical 400-m-long urban street scene and 2) a single pass around a calibration building. Each experiment succeeded at producing arc - second accurate boresight and sub-centimetre accurate lever arm parameters. Several scenarios were run. It was found that this accuracy level could be practically maintained with a control field consisting of five to seven control points dis tributed on horizontal and vertical surfaces.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".