Autonomous navigation among large number of nearby landmarks using FastSLAM and EKF-SLAM - A comparative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper compares two commonly used algorithms to solve Simultaneous Localization and Mapping (SLAM) problem in order to safely navigate an outdoor autonomous robot in an unknown location and without any access to a priori map. EKF-SLAM is considered as a classical method to solve SLAM problem. This method, however, suffers from two major issues; the quadratic computational complexity and single hypothesis data association. Large number of landmarks in the environment, especially, nearby landmarks, causes extensive error accumulation when the robot is traveling along a desired path. The multi-hypothesis data association property and the linear computational complexity are essential features in FastSLAM method. Those features make this method an alternative to overcome mentioned issues. The FastSLAM algorithm uses Rao-Blackwellised particle filtering to estimate the path of the robot and EKF-SLAM method to estimate locations of landmarks. In case of FastSLAM applications, however, observation noise needs to be reconsidered if the motion measurements are noisy while the range sensor is noiseless. This study suggests optimization of a specific situation of FastSLAM algorithm in case of noise discrepancy.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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 it