Configurable SLAM Data Pipelines Without Custom Scenario Code
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Solution Overview
Problem
Current approaches for analyzing agent data in simultaneous localization and mapping (SLAM) processes require custom code for each scenario, making it complex, time-consuming, and creating a high barrier to entry, which restricts the deployment of SLAM-based systems.
Innovation Solution
A method and system that utilize a pipeline of interconnected nodes, each representing a data processing task implemented using a software module, allowing for configuration and execution of these modules to process agent data for SLAM processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If custom code is generated for each particular scenario to analyze collected data, then the processing outcome accuracy is improved, but the device complexity and time consumption increase significantly
Solution Approach 1:
The data processing system is segmented into modular nodes, each performing a specific function (e.g., LiDAR data processing, camera data processing, fusion algorithms). These nodes can be independently configured and combined to create scenario-specific processing pipelines without writing custom code for entire systems.
Solution Approach 2:
The patent creates a universal data processing framework where the same core infrastructure supports multiple scenarios through configurable node sequences. A single system can handle different scenarios (indoor mapping, outdoor navigation, autonomous driving) by reconfiguring which nodes are activated and their connection order, eliminating the need for scenario-specific code generation.
2Adaptability or versatility
If custom code is generated for each particular scenario, then the processing adaptability is improved, but the ease of operation deteriorates due to high barrier to entry
Solution Approach 1:
The system dynamically adapts to different scenarios through configurable node sequences rather than requiring static custom code for each scenario. Users can adjust the processing pipeline by selecting and ordering pre-defined nodes based on their specific needs, making the system both adaptable and easy to operate.
Solution Approach 2:
The patent introduces an intermediary configuration layer between the user and the complex data processing algorithms. Instead of directly writing code for each scenario, users interact with a simplified configuration interface that translates high-level scenario requirements into appropriate node sequences, lowering the barrier to entry while maintaining adaptability.
3Measurement precision
If custom code is generated for each scenario, then the processing precision is improved, but the productivity decreases due to time-consuming development
Solution Approach 1:
The patent performs preliminary action by pre-defining and validating data processing nodes and their configurations before actual scenario deployment. Common processing tasks are pre-packaged as reusable nodes with optimized algorithms, so when a new scenario arises, the system can quickly assemble processing pipelines from these pre-prepared components rather than developing algorithms from scratch.
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 simplifies the processing of agent data by allowing for easy configuration and reuse of software modules, reducing the complexity and time required for data analysis, and facilitating the deployment of SLAM-based systems.
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 method for processing agent data received from one or more agents traversing an environment for use in a simultaneous localisation and mapping process. The method includes determining a pipeline including a number of nodes, and a node configuration for some of the nodes. Each node relates to a respective data processing task implemented using a respective software module, the nodes being interconnected so as to define a node sequence representing a sequence of data processing tasks. The node and pipeline are then used to load and configure software modules, which are then executed to provide output data from at least one software module as processed agent data, wherein the processed agent data is used in a simultaneous localisation and mapping process.