Distributed Localization System for Multi-Device Pose Estimation
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Solution Overview
Problem
Current Global Navigation Satellite System (GNSS) technologies fail to provide precise and robust real-time location determination of devices in challenging environments, such as dense urban areas and indoors, due to environmental conditions and limitations in scalability and robustness for multiple device communication.
Innovation Solution
A distributed localization and mapping system that uses a server system with a global map to combine sensor data from multiple devices, allowing for accurate determination of relative positions and poses of devices through local and remote resources, enabling precise and persistent real-time localization across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS technologies are used for location determination, then devices can obtain satellite-referenced positions, but precision deteriorates to around 10 metres error in challenging environments such as buildings and dense urban areas
Solution Approach 1:
The patent introduces visual features and sensor data as intermediary elements between devices and the environment. Instead of directly relying on satellite signals, the system uses cameras to capture visual features, processes this data through neural networks to determine device poses, and combines multiple sensor inputs (IMU, barometer, GPS) to achieve precise location determination in environments where GNSS fails.
Solution Approach 2:
The patent replaces the satellite-based mechanical/GNSS system with a vision-based system. Neural networks process visual features captured by device cameras to directly determine device poses and locations, substituting the satellite signal dependency with local visual feature matching and sensor fusion, thereby achieving centimeter-level precision in urban canyons and indoor environments.
2Adaptability or versatility
If classical SLAM solutions are used for multi-device localization, then devices can determine relative positions, but the systems fail to scale appropriately for large numbers of devices communicating through limited-bandwidth channels
Solution Approach 1:
The patent segments the localization problem into individual device pose determination tasks. Each device independently determines its own pose relative to the environment using its own sensors and the shared map, rather than requiring complex inter-device communication and coordination. This segmentation allows the system to scale to many devices without increasing overall system complexity.
Solution Approach 2:
Each device performs self-localization by independently processing its own sensor data and visual features through neural networks to determine its pose. Devices do not need to communicate their full state to other devices; instead, they independently determine their positions using the shared environmental map, enabling the system to scale to large numbers of devices with limited communication bandwidth.
3Device complexity
If predetermined environmental conditions and map sizes are used to avoid large storage and processing requirements, then device complexity is reduced, but the system cannot adapt to changing environments or large-scale use
Solution Approach 1:
The patent implements dynamic map generation and updating capabilities. The shared map is not static but can be continuously updated as devices explore new environments. Neural networks process visual features in real-time to adapt to changing environments, allowing the system to scale from small to large environments without redefining the entire map structure, thereby maintaining low device complexity while achieving high environmental adaptability.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable medium can receive a plurality of localization requests from a plurality of devices, each of the plurality of localization requests comprising sensor data captured by one or more sensors of the plurality of devices. Localization data can be sent to each device of the plurality of devices in response to receiving the plurality of localization requests. A plurality of pose data can be received from a first device and a second device of the plurality of devices. The plurality of pose data can include a position and orientation for each of the first and second devices based on the sensor data and the received localization data. At least one received pose data of the plurality of received pose data can be sent to at least the first device of the plurality of devices. The first device of the plurality of devices can be operable to determine a relative location of the second device in relation to the first device based on the at least one received pose data of the second device.