Distributed Device Mapping for Large-Scale Environments
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Solution Overview
Problem
Current methods for constructing globally consistent and accurate maps of large-scale environments, such as cities, are computationally and memory-intensive, limiting their applicability to small-scale data sets, and struggle with simultaneous data transfer and bandwidth restrictions in distributed systems, especially in robotics and augmented/virtual-reality applications.
Innovation Solution
A distributed localization and mapping system that determines global positions of landmarks by estimating relative device poses and landmark positions using sequential sensor data, optimizing calculations to reduce computational power and memory requirements, and efficiently distributing data across multiple devices and a central server for real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If distributed computation is used to map large-scale environments, then computational power and memory requirements are reduced, but ensuring global consistency becomes more difficult
Solution Approach 1:
The patent divides the large-scale mapping problem into smaller sub-problems that can be solved independently by multiple devices. Each device processes local sensor data to create local maps and determines its own pose, rather than requiring all devices to process all data centrally. This segmentation reduces computational burden while maintaining global consistency through coordinated data sharing and constraint propagation across the distributed system.
2Measurement precision
If millions or billions of pictures are processed for large-scale mapping, then mapping accuracy is improved, but existing methods scale poorly and become computationally infeasible
Solution Approach 1:
The patent performs preliminary processing of sensor data at the source devices before central aggregation. Each device pre-processes its local picture data to extract relevant features and constraints, then transmits only these processed results to the central system. This preliminary action at distributed nodes enables the system to handle millions or billions of pictures by reducing the data volume that requires centralized processing, thereby improving scalability while maintaining accuracy.
3Speed
If simultaneous data transfer occurs over bandwidth-limited channels, then real-time mapping is achieved, but data transfer efficiency decreases
Solution Approach 1:
The patent extracts and processes data locally at distributed devices before transmission, removing unnecessary raw data from the transfer stream. Each device extracts only the essential features and constraints from its sensor data, then transmits this condensed information to the central server. This extraction approach enables real-time processing while significantly improving bandwidth efficiency by reducing the volume of data that must be transmitted over limited channels.
Data Source
AI summary
The present invention relates to the efficient use of both local and remote computational resources and communication bandwidth to provide distributed environment mapping using a plurality of mobile sensor-equipped devices.According to a first aspect, there is provided a method of determining a global position of one or more landmarks on a global map, the method comprising the steps of determining one or more differences between sequential sensor data captured by one or more moving devices; determining one or more relative localisation landmark positions with respect to the one or more moving devices; determining relative device poses based one or more differences between sequential sensor data relative to the one or more relative localisation landmark positions; and determining a correlation between each device pose and the one or more relative localisation landmarks positions.