A framework for non-ICP LIDAR-based pose estimation using an optimally constrained 3D target
Bibliographic record
Abstract
This thesis describes a non-ICP-based framework fohr [sic] the computation of a pose estimate of a special target shape from raw LIDAR scan data. In previous work, an ideal unambiguously-shaped 3D target (the Reduced Ambiguity Cuboctahedron, or RAC) was designed for use in LIDAR-based pose estimation. The RAC was designed to be used in an ICP algorithm, without an initial guess at the pose. This property is, however, not robust to LIDAR measurement noise and data artefacts. The pose estimation technique described in the present work is based upon the geometric non-ambiguity criteria used originally to design the target, and is robust to the aforementioned LIDAR data characteristics. This technique has been tested using simulated point clouds representing a full range of views of the RAC. The technique has been validated using real LIDAR scans of the RAC, generated at Neptec's Ottawa facility with their Laser Camera System (LCS). Experimental results using LCS data show that pose estimates can be generated with mean errors (relative to ICP) of 1.03 [deg] and 1.08 [mm], having standard deviations of 0.56 [deg] and 0.67 [mm] respectively.
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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 | 0.000 | 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".