Edge-SLAM: Edge-Assisted Visual Simultaneous Localization and Mapping
Bibliographic record
Abstract
Localization in urban environments is becoming increasingly important and used in tools such as ARCore [ 18 ], ARKit [ 34 ] and others. One popular mechanism to achieve accurate indoor localization and a map of the space is using Visual Simultaneous Localization and Mapping (Visual-SLAM). However, Visual-SLAM is known to be resource-intensive in memory and processing time. Furthermore, some of the operations grow in complexity over time, making it challenging to run on mobile devices continuously. Edge computing provides additional compute and memory resources to mobile devices to allow offloading tasks without the large latencies seen when offloading to the cloud. In this article, we present Edge-SLAM, a system that uses edge computing resources to offload parts of Visual-SLAM. We use ORB-SLAM2 [ 50 ] as a prototypical Visual-SLAM system and modify it to a split architecture between the edge and the mobile device. We keep the tracking computation on the mobile device and move the rest of the computation, i.e., local mapping and loop closing, to the edge. We describe the design choices in this effort and implement them in our prototype. Our results show that our split architecture can allow the functioning of the Visual-SLAM system long-term with limited resources without affecting the accuracy of operation. It also keeps the computation and memory cost on the mobile device constant, which would allow for the deployment of other end applications that use Visual-SLAM. We perform a detailed performance and resources use (CPU, memory, network, and power) analysis to fully understand the effect of our proposed split architecture.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".