Outdoor Semantic Segmentation for UGVs Based on CNN and Fully Connected CRFs
Bibliographic record
Abstract
This paper studies semantic segmentation in outdoor scene based on multi-sensor fusion data by unmanned ground vehicle (UGV). Laser, camera, and inertial navigation are fused into RGB-DI (RGB, depth and intensity) point cloud. Because of the speed change of the UGV in outdoor scene, laser scanning points in 3D space are distributed irregularly and unbalanced. It is difficult to extract features in point cloud to describe objects accurately. Therefore, this paper proposes a projection algorithm to generate a 2D RGB-DI image from the 3D RGB-DI point cloud so that the semantic segmentation in RGB-DI cloud points is transformed to the semantic segmentation in RGB-DI images. To adequately describe multiple objects in the RGB-DI images, a convolutional neural network (CNN) model is designed to extract abstract features. Since the fully connected CRF model takes into account the context of each object location in an RGB-DI image, the fully connected CRF model is used as a classifier to complete the semantic segmentation in the RGB-DI image. According to the corresponding relation between each point in the 3D point cloud and each pixel in the RGB-DI image, segmentation results in the RGB-DI image are mapped back to the original point clouds. Different datasets are used to evaluate our algorithms. Moreover, real-world experiments were applied to our UGV platform to show the practicability and validity of the proposed approach.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".