Scan Context 3D Lidar Inertial Odometry via Iterated ESKF and Incremental K-Dimensional Tree
Bibliographic record
Abstract
This paper focused on a 3D lidar inertial odometry algorithm framework that improves the LeGO-LOAM by constructing a new back-end optimization algorithm. In comparison with the LeGO-LOAM, the feature extraction and image projection processes are still the same. Two-step Levenberg Marquardt was replaced with an iterated ESKF method in the lidar odometry to produce a better initial pose for the robots, and the k-d tree method in the lidar mapping is replaced with the ikd-Tree method to ensure high performance mapping process in real time. In the loop closure, a scan context search method is added to better correct the algorithm’s final trajectory. We compare the improved algorithm with LeGO-LOAM and the two other methods, LIO-SAM and A-LOAM, using three datasets gathered from the Mulran dataset with different large-scale outdoor scenes. We show that the improved algorithm achieves similar or better accuracy in real-time than the other three algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".