Navigating with Spatial Intelligence: A Survey of Scene Graph-Based Object Goal Navigation
Bibliographic record
Abstract
Today, autonomous mobile robots are widely used in all walks of life. Autonomous navigation, as a basic capability of robots, has become a research hotspot. Classical navigation techniques, which rely on pre-built maps, struggle to cope with complex and dynamic environments. With the development of artificial intelligence, learning-based navigation technology have emerged. Instead of relying on pre-built maps, the agent perceives the environment and make decisions through visual observation, enabling end-to-end navigation. A key challenge is to enhance the generalization ability of the agent in unfamiliar environments. To tackle this challenge, it is necessary to endow the agent with spatial intelligence. Spatial intelligence refers to the ability of the agent to transform visual observations into insights, insights into understanding, and understanding into actions. To endow the agent with spatial intelligence, relevant research uses scene graph to represent the environment. We refer to this method as scene graph-based object goal navigation. In this paper, we concentrate on scene graph, offering formal description, computational framework of object goal navigation. We provide a comprehensive summary of the methods for constructing and applying scene graph. Additionally, we present experimental evidence that highlights the critical role of scene graph in improving navigation success. This paper also delineates promising research directions, all aimed at sharpening the focus on scene graph. Overall, this paper shows how scene graph endows the agent with spatial intelligence, aiming to promote the importance of scene graph in the field of intelligent 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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".