Enhancing Body-Mounted LiDAR SLAM using an IMU-based Pedestrian Dead Reckoning (PDR) Model
Bibliographic record
Abstract
Simultaneous localization and mapping for human body-mounted platforms have been recently the focus of navigation research to support a wide range of applications such as rescue, first-responders, mining, and defense. For vehicular platforms, wheel odometry has been used to enhance the accuracy of SLAM. However, wheel odometry is not available in body-mounted platforms. Using raw inertial measurement unit (IMU) as odometry is not accurate enough to support SLAM due to the large and rapid drifts caused by IMU data integration. To address this challenge, we propose a sensor fusion scheme for body-mounted SLAM that integrates the IMU-based Pedestrian Dead Reckoning (PDR) model with a low-cost lightweight 2D LiDAR sensor. In the proposed fusion, the PDR model is used as a replacement for wheel odometry in vehicular platforms. A system prototype consisting of a helmet-mount IMU from Xsens and RPLIDAR A1 2D LiDAR sensor has been developed and used for field data collection. The developed PDR model was integrated into the Cartographer SLAM engine and compared with Hector SLAM. Our experiments demonstrated that the integration of PDR has enhanced the SLAM accuracy and contributed in bridging featureless portions of the environment leading to an overall average improvement of 71.47%.
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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 | 0.001 |
| 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".