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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".