Purpose of the project
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Inspired by the concept of MLOps, design the concept of SLAMOps to a level where the entire process of SLAM can be modularized/automated and applied to real services.
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Phase 0: SLAM on Docker
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Docker containerization to run SLAM algorithm in virtualized environment
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Phase 1: Hyper-parameter tuning
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Configure SLAM mapping hyper-parameter tuning pipeline using Katib in Kubeflow
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Phase 2: Experiment Pipeline
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Develop many ideas for SLAM and run them in isolated Docker containers
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Logs output as tasks are executed, outputs saved when tasks are completed, and
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Modularized to ensure results are reproducible and reusable
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Phase 3: Continuous Mapping
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Consider the maps generated as a result of SLAM as one deep learning model
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The world we live in is constantly changing, so maps need to be constantly updated.
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We envision a concept of continuous mapping that includes concepts such as map update, fleet management, etc.
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Phase 4: Localization Serving
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Localization, which identifies one's location on a mapped map, is similar to the task of performing inference on a model trained in deep learning.
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As the number of robots to be managed increases, it is necessary to introduce the concept of large-scale distributed processing to stably provide localization services.
Contributions
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Studying MLOps
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Initial conceptual design of SLAMOps
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Phase 0: SLAM on Docker Content Creation
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Phase 1: Hyper-parameter tuning
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Implementing metrics to evaluate the quality of the maps resulting from SLAM
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Reference paper: https://arxiv.org/abs/2101.10402