SLAM Graph Optimization via Node Removal
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Solution Overview
Problem
Conventional SLAM techniques face inefficiencies in managing SLAM graphs, leading to increased computational complexity and memory usage, particularly in dynamic environments, where frequent updates and reorganization of graph structures are necessary to maintain accurate localization and mapping.
Innovation Solution
A method for updating and optimizing SLAM graphs by identifying and removing pose nodes and edges based on thresholds, using Markov blanket nodes and residual values to reduce graph complexity, while also adding new nodes and edges for landmark recognition and tracking, thereby improving operational speed and information retention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the SLAM graph is continuously updated to maintain accurate localization and mapping in dynamic environments, then the accuracy and adaptability improve, but the computational complexity and memory usage increase
Solution Approach 1:
The patent extracts and removes pose nodes and edges from the SLAM graph that no longer contribute to accurate localization, thereby reducing computational complexity while maintaining reliability. This is achieved through threshold-based removal of redundant graph elements.
Solution Approach 2:
The patent dynamically adjusts graph structure parameters (number of pose nodes and edges) based on operational thresholds and residual values, changing the state of the SLAM graph to balance accuracy requirements with computational constraints in real-time.
2Adaptability or versatility
If the SLAM graph structure is frequently updated and reorganized to adapt to dynamic environments, then the adaptability improves, but the processing time and computational load increase
Solution Approach 1:
The patent applies local quality by performing targeted updates only in specific regions of the SLAM graph where changes are necessary, rather than reorganizing the entire graph structure. This reduces processing time while maintaining adaptability to environmental changes.
Solution Approach 2:
The patent performs partial updates to the SLAM graph by selectively adding or removing only the necessary pose nodes and edges based on current operational needs, avoiding complete graph reorganization and thereby reducing computational overhead.
3Loss of information
If more pose nodes and edges are retained in the SLAM graph to preserve information content, then the mapping accuracy improves, but the memory usage and computational burden increase
Solution Approach 1:
The patent uses residual values as feedback to determine whether pose nodes and edges should be retained or removed from the SLAM graph. This feedback mechanism ensures that information retention is optimized by keeping only those graph elements that contribute significantly to localization accuracy.
Solution Approach 2:
The patent discards redundant pose nodes and edges that no longer contribute to accurate localization, freeing memory resources while recovering and preserving critical graph elements through selective retention based on threshold criteria.
Data Source
AI summary
The invention is related to methods and apparatus that use a visual sensor and dead reckoning sensors to process Simultaneous Localization and Mapping (SLAM). These techniques can be used in robot navigation. Advantageously, such visual techniques can be used to autonomously generate and update a map. Unlike with laser rangefinders, the visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments, such as environments in which people move. Certain embodiments contemplate improvements to the front-end processing in a SLAM-based system. Particularly, certain of these embodiments contemplate a novel landmark matching process. Certain of these embodiments also contemplate a novel landmark creation process. Certain embodiments contemplate improvements to the back-end processing in a SLAM-based system. Particularly, certain of these embodiments contemplate algorithms for modifying the SLAM graph in real-time to achieve a more efficient structure.