A kinect-based SLAM in an unknown environment using geometric features
Bibliographic record
Abstract
This paper proposes a geometric feature-based method to solve the Simultaneous Localization and Mapping (SLAM) problem in an unknown structured environment using a short range and low Field of View (FoV) measurement unit such as Kinect sensor. A RANdom SAmple Consensus (RANSAC) based algorithm is used for feature detection, and a grid-based point cloud segmentation method has been introduced to improve the multiple feature point-detection in a 2D depth frame. A fast SLAM algorithm is used to estimate the robot posterior and the map of the environment. This approach builds the individual maps for each particle using geometric features that are extracted from a 2D slice of a 3D depth image. Each map contains individual Extended Kalman Filters (EKFs) for each and every feature-point. This method reduces the uncertainty of the robot pose in the prediction step and it improves the pose accuracy when more geometric feature-points are available. The proposed feature-based approach gives better localization and compact map representation in structured environments when distinct features are available. The importance weighting and the comparison of features with the on-line map are performed according to the maximum likelihood criterion. In order to reduce the particle depletion, the map is updated only when a new Odometry measurement and new range measurements are available. The experiments are carried out using the recorded data with a non-holomonic mobile robot equipped with a Kinect sensor in a small scale indoor structured environment. For comparison, the grid based SLAM result is also presented for the same data set.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".