Manhattan World Constraint for Indoor Line-based Mapping Using Ultrasonic Scans
Bibliographic record
Abstract
Ultrasonic rangefinders are low-cost sensors that have been utilized in the past to perform mapping, localization and Simultaneous Localization and Mapping (SLAM). Ultrasonic has a low angular resolution, and it is challenging to identify and correspond features in ultrasonic scans. In the past, occupancy mapping has been proposed to address this issue. However, for the localization of the sensor with respect to such occupancy grids, post-processing for landmark identification is required (which increases the computational cost). In contrast to the occupancy grid approach, direct feature-based mapping methods are also proposed in the literature, where landmarks are extracted using many consecutive ultrasonic sensor scans instead of using a single sparser scan. In this article, a novel direct feature-based mapping method using ultrasonic sensors in the indoor manmade environment is proposed. The proposed method utilizes the prior knowledge of the structure of indoor environments (such as most houses or warehouses), where the walls are either parallel or perpendicular (Manhattan World Constraint (MWC)). This prior knowledge is used with multiple consecutive ultrasonic scans to provide accurate line-based maps. Compared to the occupancy grid mapping, the proposed method avoids the discretization of the point cloud coordinates (which can be a source of inaccuracy). Further, it does not require a second step for landmark detection. Compared to the direct feature-based methods, which are general-purpose, our approach is the first method- to the best of the authors' knowledge- that imposes MWC to map using panoramic ultrasonic scans. The accuracy of the proposed mapping method is demonstrated in a cluttered indoor environment. The results indicate that the proposed method accurately detects and distinguishes most edges and corners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".