Introduction
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a cross-device edge-cloud collaborative offloading framework for autonomous vehicle camera relocalization eases the requirements of autonomous vehicles for high-performance computing equipment,
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the advantages of our proposed framework in terms of computational efficiency are demonstrated through simulation experiments on an advanced MapNet series relocalization model.
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by improving the two performance indicators of frequency and route, we demonstrate the prospects of our framework in multi-source information fusion.
Related work
MapNet series camera relocation scheme
Mobile edge computing offloading computing resources
Methods
Overview of DNN-based Relocalization Module
Challenges in DNN-based Relocalization Module
Offloading strategy
Experimental Results
Setup
Dataset
Experimental details
Network split
local inference to | null | conv1 | bn1 | relu | maxpool |
---|---|---|---|---|---|
average of 100 frames time/s | 0.4710 | 1.0022 | 1.0804 | 1.0672 | 0.6140 |
single frame time/s | 0.5612 | 1.2357 | 1.1516 | 1.5340 | 0.6589 |
local inference to | layer1 | layer2 | layer3 | layer4 | avgpool | fc |
---|---|---|---|---|---|---|
average of 100 frames time/s | 0.7287 | 0.7480 | 1.0426 | 1.1310 | 1.1010 | 1.1099 |
single frame time/s | 0.8266 | 0.7595 | 0.8537 | 0.9657 | 0.8700 | 0.8609 |
layer | Conv1 | Bn1 | Layer1 | Layer2 | Layer3 | Layer4 | Fc |
---|---|---|---|---|---|---|---|
params | 9408 | 128 | 221,952 | 1,116,416 | 6,822,400 | 13,114,368 | 1,050,624 |