NEW METHOD FOR THE CALIBRATION OF MULTI-CAMERA MOBILE MAPPING SYSTEMS
Bibliographic record
Abstract
Abstract. Mobile Mapping Systems (MMS) allow for fast and cost-effective collection of geo-spatial information. Such systems integrate a set of imaging sensors and a position and orientation system (POS), which entails GPS and INS units. System calibration is a crucial process to ensure the attainment of the expected accuracy of such systems. It involves the calibration of the individual sensors as well as the calibration of the mounting parameters relating the system components. The mounting parameters of multi-camera MMS include two sets of relative orientation parameters (ROP): the lever arm offsets and the boresight angles relating the cameras and the IMU body frame and the ROP among the cameras (in the absence of GPS/INS data). In this paper, a novel single-step calibration method, which has the ability of estimating these two sets of ROP, is devised. Besides the ability to estimate the ROP among the cameras, the proposed method can use such parameters as prior information in the ISO procedure. The implemented procedure consists of an integrated sensor orientation (ISO) where the GPS/INS-derived position and orientation and the system mounting parameters are directly incorporated in the collinearity equations. The concept of modified collinearity equations has been used by few authors for single-camera systems. In this paper, a new modification to the collinearity equations for GPS/INS-assisted multicamera systems is introduced. Experimental results using a real dataset demonstrate the feasibility of the proposed method.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".