Experimental Analysis of the Behavior of Mirror-like Objects in LiDAR-Based Robot Navigation
Bibliographic record
Abstract
Mobile robots are equipped with various sensors to perform object detection, localization, and navigation. Among these sensors, LiDAR (light detection and ranging) is the most widely used sensor for environment map creation. However, LiDAR-based localization is challenging in modern environments containing specular surfaces, such as mirrors and glasses, that cause light reflection, penetration, or diffusion. These conditions make the obtained map inaccurate, unreliable, and noisy. This paper presents the effects of mirror-like objects in various indoor arrangements on 2D LiDAR-based maps. Experiments were conducted using a mobile robot equipped with LiDAR navigating in an environment with several mirrors. Experiments suggest that laser scans may be fully reflected off mirrors, causing no range or intensity data and creating a faulty map. Objects or boundaries within the range of LiDAR may be mapped behind the surface of the mirror, and robot self-detection may occur on the surface of the mirror. This situation exacerbates when more than one mirror is present in the environment. The results presented in this paper can aid the development of LiDAR-based indoor navigation to identify and remove inconsistencies created in LiDAR maps due to mirror-like objects.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".