2D LIDAR and mmWave-Based Mapping and Localization for Indoor Navigation
Bibliographic record
Abstract
The use of wearable devices has grown exponentially, with applications ranging from improving efficiency to localization and mapping for situational awareness. Yet commercial devices are unavailable for emergency response. Previous researchers at the BioMechatronics lab in partnership with the Oshawa fire department developed a low-cost wearable for accurate localization and mapping of firefighters. The wearable was equipped with a 2D-LIDAR, IMU, and mmWave sensor, but available software failed because the user's gait corrupted the mapping and environmental conditions were inappropriate for some sensors. This thesis proposes a solution to enable accurate mapping with 2D-LIDAR subjected to gait-induced out-of-plane motion. It also introduces two three-degrees-of-freedom localization algorithms using a mmWave sensor to enable localization in environments where LIDAR performance is degraded. These localization algorithms are then extended to six-degrees-of-freedom to fully capture the wearable's movement. Together, these advancements pave the way for reliable adoption of wearable devices by emergency responders.
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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) | 0.000 | 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) | 0.004 | 0.002 |
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".