Predictive Localization Models for Distributed Mapping Workloads
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Solution Overview
Problem
Localization and mapping processes, particularly in resource-constrained mobile devices, are computationally demanding and require efficient workload distribution among devices in a network to optimize performance and resource usage.
Innovation Solution
A predictive model is generated and deployed for each localization-related device based on its properties, allowing for workload allocation and performance prediction, using a predictor device to determine and adapt models for individual devices within a network, considering hardware properties and observed performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If localization and mapping tasks are performed on resource-constrained mobile devices, then device autonomy and flexibility are improved, but computational performance and energy efficiency deteriorate
Solution Approach 1:
The system segments the computational workload by dividing it into multiple tasks that can be distributed across different devices in a network. Each device performs only the portion of localization and mapping tasks assigned to it, rather than requiring a single device to handle all computations independently. This segmentation enables resource-constrained mobile devices to participate in localization tasks without being overwhelmed by the full computational burden.
Solution Approach 2:
The system creates a multi-functional network where various types of devices (mobile devices, stationary devices, devices with different computational capabilities) can all contribute to localization and mapping tasks. Each device leverages its specific properties (camera quality, processing power, energy source) to perform appropriate functions, making the overall system universally capable while individual devices remain adapted to their resource constraints.
2Productivity
If computational tasks are distributed across a network of devices, then overall system performance is improved, but system complexity increases
Solution Approach 1:
The system dynamically adjusts task allocation parameters based on observed device properties and performance metrics. By monitoring actual device behavior and changing resource allocation decisions in real-time, the system manages the complexity of distributed computation through adaptive parameter adjustment rather than requiring complex pre-planned task distribution strategies.
Solution Approach 2:
The system implements feedback mechanisms where device performance is continuously monitored and used to inform future task allocation decisions. This feedback loop allows the system to automatically adapt to changing conditions and device capabilities, managing network complexity through self-regulation rather than requiring manual configuration or complex centralized control.
3Measurement precision
If predictive models are customized for each device based on its properties, then task allocation accuracy is improved, but model generation and deployment complexity increases
Solution Approach 1:
The system creates simplified predictive models for each device by copying and adapting a base model structure, rather than developing entirely unique complex models for each device. The models are generated by interpolating and extrapolating from known device properties, creating lightweight predictive representations that capture essential device characteristics without requiring full-blown custom model development for each device.
Solution Approach 2:
The predictive models use device-specific parameters (hardware properties, energy source, observed performance) to adjust task allocation predictions. By changing model parameters based on device properties rather than restructuring entire models, the system achieves accurate device-specific predictions while keeping model complexity manageable through parameter adjustment rather than architectural complexity.
Data Source
AI summary
Embodiments include methods performed by a predictor device for facilitating predictions related to localization and/or mapping performance of one or more devices of a device network. Such methods include detecting that a first localization-related device has joined the device network and obtaining a first set of properties of the first localization-related device. Such methods also include determining, based on the first set of properties, a predictive model for the first localization-related device and deploying the predictive model for the first localization-related device. Other embodiments include predictor devices configured to perform such methods.