Enhancing Body-Mounted LiDAR SLAM using an IMU-based Pedestrian Dead Reckoning (PDR) Model
Bibliographic record
Abstract
With the significant reduction in motion sensors' cost and power, Simultaneous Localization and Mapping (SLAM) has emerged as a core technology that powers up a wide range of applications such as virtual/augmented reality, search-and-rescue, firstresponders, mining, and defence.Existing SLAM methods have been designed mainly for robotic platforms that use wheel odometry as a system motion model.However, wheel odometry is not available for body-mounted platforms, which limits the accuracy of SLAM algorithms when implemented on such platforms.This thesis addresses the challenge of body-mounted SLAM by proposing an integrated sensor fusion scheme.A Pedestrian Dead Reckoning (PDR) model based on the Inertial Measurement Unit (IMU) is used to enhance LiDAR-based SLAM.This proposed fusion uses the PDR model as a replacement for wheel odometry in vehicular platforms.A system prototype has been developed and used for data collection and experiments.Plus, a PDR model was implemented and integrated into the Google Cartographer SLAM engine and tested against different positioning systems such as stand-alone IMU-PDR, Hector SLAM and Cartographer.Experiments demonstrated that the integration of PDR has significantly enhanced head-mounted SLAM accuracy leading to accurate positioning under different motion 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".