SLAM-TSM: Enhanced Indoor LiDAR SLAM With Total Station Measurements for Accurate Trajectory Estimation
Bibliographic record
Abstract
Simultaneous Localization and Mapping (SLAM) is a crucial task in various domains, including intelligent robotics, computer vision, and indoor navigation. Accurate and robust trajectory estimation is especially challenging in indoor environments due to the presence of feature-poor or repetitive scenes, limited visibility, and dynamic objects. Obtaining highly accurate machine platform odometry is also an important basis for solving the “last mile” problem in intelligent transportation. This paper proposes a novel algorithm that combines LiDAR-based SLAM, total station measurements, and graph optimization to optimize the robot’s trajectory in indoor environments. By integrating highly accurate positional data from total station measurements as additional constraints, the proposed method enhances the performance of indoor LiDAR SLAM, effectively addressing the challenges of drift and trajectory offsets. Moreover, the proposed algorithm can provide trajectory optimization even in the absence of loop closure detection, making it more robust and suitable for a broader range of indoor environments. Experimental results validated the effectiveness of the proposed approach in reducing drift and improving trajectory estimation for low-cost indoor LiDAR devices, demonstrating its potential in various applications such as autonomous navigation, facility management, and augmented reality. It provides targeted ground truth for autonomous driving of machine platforms in intelligent transportation 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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".