Comparison of 2D Localization Using Radar and LiDAR in Long Corridors
Bibliographic record
Abstract
Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) systems are often used to map indoor areas and localize mobile systems. LiDAR scan matching performs poorly in environments without rich geometry, such as long hallways or large rooms. LiDAR scan matching often loses confidence in estimating pose along the direction of the hallways or walls. To address this limitation, a mm-wave fixed antenna array radar is proposed as a low-cost aid towards maintaining full pose information during these situations. Localization is performed using a scan matching algorithm that is developed through a correlative method. We provide experimental results and analysis for the scan matching performance of each sensor in a long hallway scenario. Experiments showed that radar scan matching can maintain better pose confidence in the direction of a straight hallway where a similar LiDAR system commonly fails. Therefore, a combination of LiDAR/radar could be ideal for indoor navigation in long corridors and large rooms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".