HoloSLAM: a novel approach to virtual landmark-based SLAM for indoor environments
Bibliographic record
Abstract
Abstract In this paper, we present HoloSLAM which is a novel solution to landmark detection issues in the simultaneous localization and mapping (SLAM) problem in autonomous robot navigation. The approach integrates real and virtual worlds to create a novel mapping robotic environment employing a mixed-reality technique and a sensor, namely Microsoft HoloLens. The proposed methodology allows the robot to interact and communicate with its new environment in real-time and overcome the limitations of conventional landmark-based SLAMs by creating and placing some virtual landmarks in situations where real landmarks are scarce, non-existent, or hard to be detected. The proposed approach enhances the robot’s perception and navigation capabilities in various robot environments. The overall process contributes to the robot’s more accurate understanding of its environment; thus, enabling it to navigate with greater efficiency and effectiveness. In addition, the newly implemented HoloSLAM offers the option to guide the robot to a specific location eliminating the need for explicit navigation instructions. The open-source framework proposed in this paper can benefit the robotics community by providing a more reliable, realistic, and robust mapping solution. The experiments show that the Ellipsoidal-HoloSLAM system is accurate and effectively overcomes the limitations of conventional Ellipsoidal-SLAMs, providing a more precise and detailed mapping of the robot’s environment.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".