Indoor 3D Semantic Robot VSLAM Based on Mask Regional Convolutional Neural Network
Bibliographic record
Abstract
During the construction of indoor environmental semantic maps by robot Vision SLAM (VSLAM), there exist some problems such as low label classification accuracy and low precision under the situation of sparse feature points. In this case, this paper proposes an indoor three-dimensional semantic VSLAM algorithm based on Mask Regional Convolutional Neural Network (RCNN). Firstly, an Oriented FAST and a Rotated BRIEF (ORB) algorithms are used to extract image feature points. Secondly, a Random Sample Consensus (RANSAC) algorithm is employed to eliminate mismatched points and estimate camera position-pose changes. Then, a Mask RCNN algorithm is applied to make partial adjustments to its hyper parameter. A self-made data set is used to transfer learning, fulfilling real-time target detection and instance segmentation of a scene. A three-dimensional semantic map is constructed in combination with VSLAM algorithm. The semantic information in the environment not only improves the accuracy of VSLAM construction and positioning, but also reduces the impact of object movement on the construction by marking movable objects. Meanwhile, the VSLAM algorithm is used to calculate the positional constraints between objects and improve the accuracy of semantic understanding. Finally, by comparing with other methods, it demonstrates that this method is more correct and effective. It was also verified that the proposed method can accurately interpret the semantic information in environment for the construction of three-dimensional semantic maps.
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 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 | 0.000 |
| 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".