Region-Specific SLAM Parameter Tuning for Accurate Navigation
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Solution Overview
Problem
Existing SLAM algorithms struggle to optimize performance across diverse environments due to varying conditions such as lighting, obstacles, and feature availability, leading to inconsistent navigation accuracy and resource inefficiency.
Innovation Solution
A method for configuring SLAM-based systems by obtaining 3D models of specific regions, collecting visual data under different operational conditions, and applying multiple sets of SLAM parameter values to generate and evaluate trajectories, determining an optimal set for accurate navigation and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a universal SLAM parameter configuration is used, then the system can operate in multiple environments, but navigation accuracy deteriorates in specific environments with varying conditions
Solution Approach 1:
The patent applies local quality by determining region-specific SLAM parameters based on the characteristics of each environment (e.g., indoor vs. outdoor, lighting conditions, feature density). Instead of using a single universal parameter set, the system selects or generates optimized parameters tailored to each specific region, thereby maintaining high navigation accuracy across diverse environmental conditions while preserving versatility.
Solution Approach 2:
The system dynamically adapts SLAM parameters based on real-time or pre-determined environmental characteristics. The parameter selection process is made dynamic by evaluating environment type, lighting conditions, and feature availability, allowing the system to switch between different parameter configurations as needed to maintain optimal navigation performance across varying environments.
2Measurement precision
If multiple SLAM parameter sets are tested and evaluated, then optimal performance is achieved for specific regions, but computational resources and time are consumed
Solution Approach 1:
The patent applies preliminary action by pre-determining and storing region-specific SLAM parameter configurations before actual navigation occurs. The system pre-evaluates different parameter sets for various environment types and saves the optimized parameters, so that during actual operation, the system can quickly retrieve and apply the pre-optimized parameters without performing time-consuming real-time optimization, thus reducing computational time while maintaining high trajectory accuracy.
Solution Approach 2:
The system optimizes computational efficiency by changing parameters in a structured manner - pre-defining discrete parameter sets corresponding to different environment types and conditions. This allows the system to switch between predefined parameter configurations rather than performing continuous optimization, significantly reducing the time and computational resources required while achieving optimal performance for each region type.
3Adaptability or versatility
If SLAM algorithms process visual data in diverse lighting and environmental conditions, then navigation capability is maintained, but system reliability decreases due to inconsistent performance
Solution Approach 1:
The patent addresses reliability by implementing local quality through environment-specific parameter optimization. The system identifies the specific environmental characteristics of each region (lighting conditions, feature density, obstacles) and selects or generates SLAM parameters tailored to those conditions. This ensures consistent and reliable navigation performance within each environment type, as the parameters are optimized for the specific challenges of that environment rather than using generic parameters that may fail under certain conditions.
Data Source
AI summary
Some embodiments are directed toward a computerized method for configuring navigation computer code for a SLAM based system. The method can include applying SLAM to recordings of a visual data (VD) collector traversing the region at different operation conditions using a variety of sets of SLAM parameter values, generating corresponding trajectories assessing collection movements of the collector within the region; evaluating performance of the different sets of SLAM parameter values under that different operational conditions, based on accuracy of the respective trajectories with respect to a 3D model; based at least on the plurality of performance evaluations for the different operational conditions, determining an operational set of SLAM parameter values for a SLAM-based system; and providing the operational set of SLAM parameter values to the SLAM-based system equipped with VD processor.