Multi-Agent 3D Map Graphs for Real-Time Accurate Mapping
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Solution Overview
Problem
Existing systems for generating environment maps using multiple agents face inaccuracies due to inertial sensor drift and limitations in range sensing, leading to incomplete and time-consuming mapping processes, especially in challenging environments, and offline batched approaches are not scalable for real-time applications.
Innovation Solution
A system and method where multiple agents acquire and share mapping data using range sensors to generate frames and graphs, with each agent maintaining an independent graph based on local trajectories and shared frames, allowing for real-time, globally consistent map generation and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline batched approaches are used to generate a single global map, then map accuracy is improved, but scalability and real-time performance deteriorate due to linear increase in computation requirements
Solution Approach 1:
The patent divides the global mapping problem into multiple independent local maps, each maintained by individual agents. Each agent processes and stores its own local map data separately, enabling parallel processing and eliminating the need for centralized batch processing of entire environments. This segmentation allows real-time updates while maintaining accuracy through distributed computation.
2Area of stationary object
If a single global map is generated using multiple agents, then comprehensive coverage is achieved, but computation requirements increase linearly making it infeasible for large-scale applications
Solution Approach 1:
The patent segments the environment into multiple regions, each covered by a different agent maintaining its own local map. This distributes the computational burden across multiple independent agents rather than requiring one agent to process the entire environment, enabling scalable mapping of large areas without linear increase in individual computation requirements.
Solution Approach 2:
Each agent is designed to be self-sufficient, maintaining and updating its own local map independently while also being able to share data with other agents. This multi-functionality allows agents to operate autonomously for local mapping while collaborating for global coverage, reducing overall system complexity.
3Device complexity
If a single agent traverses the environment to generate a map, then computational resources are minimized, but mapping time increases and coverage is limited by the agent's traversal capabilities
Solution Approach 1:
The patent merges the mapping capabilities of multiple agents working in parallel, where each agent contributes to the overall map coverage simultaneously. This combines computational resources across multiple agents to achieve faster mapping speeds and broader coverage while maintaining individual agent autonomy and resource efficiency.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid, accurate, and scalable mapping of environments by agents, improving map accuracy and coverage, even in complex terrains, and allows for real-time navigation and mapping in large-scale environments.
Implementation Method 1
a range sensor, such as a LiDAR range scanner, which measures reflected laser light to find the range of part of the environment
Data Source
AI summary
A system for generating a map of an environment, the system including a plurality of agents that acquire mapping data captured by a mapping system including a range sensor. The mapping data is indicative of a three dimensional representation of the environment and is used to generate frames representing parts of the environment. The agents receive other frame data from other agents, which is indicative of other frames representing parts of the environment generated using other mapping data captured by a mapping system of the other agents. Each agent then generates a graph representing a map of the environment by generating nodes using the frames and other frames, each node being indicative of a respective part of the environment, and calculating edges interconnecting the nodes, the edges being indicative of spatial offsets between the nodes.