Bibliographic record
Abstract
Abstract. Accurate LiDAR odometry results contribute directly to high-quality point cloud maps. However, traditional LiDAR odometry methods drift easily upward, leading to inaccuracies and inconsistencies in the point cloud maps. Considering abundant and reliable ground points in the Mobile Mapping System(MMS), ground points can be extracted, and constraints can be built to eliminate pose drifts. However, existing LiDAR-based odometry methods either do not use ground point cloud constraints or consider the ground plane as an infinite plane (i.e., single ground constraint), making pose estimation prone to errors. Therefore, this paper is dedicated to developing a Multiple Ground Constrained LiDAR Odometry(M-GCLO) method, which extracts multiple grounds and optimizes those plane parameters for better accuracy and robustness. M-GCLO includes three modules. Firstly, the original point clouds will be classified into the ground and non-ground points. Ground points are voxelized, and multiple ground planes are extracted, parameterized, and optimized to constrain the pose errors. All the non-ground point clouds are used for point-to-distribution matching by maintaining an NDT voxel map. Secondly, a novel method for weighting the residuals is proposed by considering the uncertainties of each point in a scan. Finally, the jacobians and residuals are given along with the weightings for estimating LiDAR states. Experimental results in KITTI and M2DGR datasets show that M-GCLO outperforms state-of-the-art LiDAR odometry methods in large-scale outdoor and indoor scenarios.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".