LiDAR-to-Map Registration: Comparative Analysis of Mechanical and Solid-State LiDAR Technologies Across ICP and NDT Algorithms
Bibliographic record
Abstract
Accurate positioning is essential for autonomous vehicle (AV) navigation systems, supporting tasks such as motion planning, decision-making, and control. Although the Global Navigation Satellite System (GNSS) is widely used to provide positioning services, its accuracy and reliability can degrade and may even become unavailable in urban and indoor environments. However, AVs must have access to a positioning solution in all environments at all times. To bridge occurrences of GNSS unreliability, researchers have investigated the use of perception sensors such as LiDAR to sense the environment around the AV and provide an alternative positioning solution. LiDAR-based positioning methods, including LiDAR odometry (LO) and map matching, typically rely on registration algorithms such as Iterative Closest Point (ICP) and Normal Distribution Transform (NDT) for pose estimation. Mechanically Spinning LiDAR (MSL) is a well-established LiDAR technology that has been mounted on AVs to assist in tasks such as positioning and mapping. More recently, Solid-State LiDAR (SSL) has emerged as a promising new LiDAR technology, offering advantages over MSL in the form of fewer moving parts and lower costs. This study introduces a LiDAR-to-Map Registration (LMR) pipeline designed to evaluate the performance of MSL and SSL for AV positioning using real-world data. The comparative analysis investigates the suitability of each LiDAR technology accross various dynamic driving scenarios and through diverse environments, highlighting their strengths in the scenarios challenging to GNSS positioning.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".