Dynamic Self-Maintenance Obstacle Costmap Based on Instance Segmentation and Memory Storage Mechanism
Bibliographic record
Abstract
As the key component of graph search path planning algorithms, environmental costmap is widely used in many robot navigation systems. However, during the lifelong operation of robot, various types of obstacles change the environment constantly, which affect the accuracy of costmap and the effectiveness of path planning. To eliminate the negative effects of obstacle changes in lifelong navigation, we propose a novel dynamic self-maintenance obstacle costmap based on instance segmentation and memory storage mechanism. First, we present an instance-level obstacle segmentation model based on laser-vision fusion. The pose, category, and observation information of obstacles are recorded through clustering and recognition. Then, we suggest an update and maintenance approach for obstacle costmap based on memory mechanism. Imitating the Ebbinghaus Forgetting Curve, the costs are updated by different activity levels in real-time, and obstacles are removed or retained in costmap accurately. In comparison to the widely used layered costmaps, experiments show that the proposed approach is able to remove the obsolete obstacles out of sight in time, enhance the accuracy of costmap and improve the effectiveness of lifelong navigation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".