Cooperative 3D Mapping Using Multiple Mobile Devices
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Solution Overview
Problem
Creating an accurate 3D map in GPS-denied areas using a single mobile device is challenging due to resource constraints and the need for repeated mapping when conditions change, and existing technologies struggle with merging data from multiple users with unknown starting poses.
Innovation Solution
The proposed solution involves cooperative mapping techniques that merge independently estimated trajectory and map data from multiple vision-aided inertial navigation devices using geometric constraints, formulating the problem as a constrained optimization problem, and efficiently solving it using batch-least-squares methods, which are less memory demanding and parallelizable, allowing for trade-offs between accuracy and processing cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single mobile device is used for mapping, then device complexity is reduced, but the mapping area and accuracy are limited due to resource constraints
Solution Approach 1:
The patent combines mapping data from multiple mobile devices through data fusion techniques. Multiple devices independently capture visual and inertial data, then their trajectories and map data are merged using geometric constraints and optimization algorithms to create a comprehensive large-scale 3D map that exceeds the capabilities of any single device.
Solution Approach 2:
The mapping task is divided into independent segments performed by different mobile devices. Each device independently collects and processes its own trajectory and map data, which are then integrated through the cooperative mapping framework. This segmentation allows parallel data collection across multiple devices while maintaining individual processing efficiency.
2Area of stationary object
If multiple mobile devices are used for cooperative mapping, then mapping area and accuracy are improved, but data merging complexity increases when starting poses are unknown
Solution Approach 1:
The patent introduces an optimization-based intermediary framework that mediates the merging of data from multiple devices with unknown starting poses. Geometric constraints serve as intermediaries, linking trajectories and map data across devices through commonly observed features. The batch least-squares optimization algorithm acts as a mediator to simultaneously solve for all device poses and map features, resolving the complexity of merging unknown reference frames.
Solution Approach 2:
The system transforms the merging problem by changing parameters from absolute poses to relative geometric constraints. Instead of requiring known starting poses, the optimization framework works with relative geometric relationships between commonly observed features across devices. This parameter transformation enables solving for absolute poses as emergent results rather than required inputs.
3Measurement precision
If all common-feature constraints are retained for accurate merging, then mapping accuracy is improved, but processing cost and memory usage increase
Solution Approach 1:
The patent applies partial action by selectively retaining only the most informative subset of common-feature constraints rather than processing all possible constraints. The system identifies and prioritizes constraints that provide the most geometric information for accurate pose estimation, discarding redundant constraints. This partial processing maintains mapping accuracy while significantly reducing computational burden and memory requirements.
Solution Approach 2:
Different constraints are treated with different levels of processing priority based on their local quality and informativeness. The system evaluates each common-feature constraint's contribution to the overall mapping accuracy and applies processing selectively. High-value constraints are retained and processed in detail, while low-value constraints are discarded or processed approximately, creating a quality-based processing hierarchy.
Data Source
AI summary
A method includes: receiving, with a computing platform, respective trajectory data and map data independently generated by each of a plurality of vision-aided inertial navigation devices (VINS devices) traversing an environment, wherein the trajectory data specifies poses along a path through the environment for the respective VINS device and the map data specifies positions of observed features within the environment as determined by an estimator executed by the respective VINS device; determining, with the computing platform and based on the respective trajectory data and map data from each of the VINS devices, estimates for relative poses within the environment by determining transformations that geometrically relate the trajectory data and the map data between one or more pairs of the VINS devices; and generating, with the computing platform and based on the transformations, a composite map specifying positions within the environment for the features observed by the VINS devices.