SLAM Optimization for Mobile Robots Using Erroneous Particles
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Solution Overview
Problem
Simultaneous Localization and Mapping (SLAM) techniques are computationally intensive, making them costly to implement in consumer products with limited processing power and memory capacity, necessitating optimization methods to ensure efficient execution without compromising performance.
Innovation Solution
The implementation of optimization methods, including suspending robot pose updates during delocalization, introducing erroneous particles to detect delocalization, treating dynamic areas of the map, and addressing tilt conditions through software and hardware approaches, to reduce computational load and maintain map accuracy in low-cost hardware environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robust SLAM algorithms are implemented to ensure accurate mapping and localization, then mapping precision and localization accuracy are improved, but computational load and hardware cost increase
Solution Approach 1:
The patent segments the particle filter algorithm into multiple independent particles, where each particle represents a hypothesis about robot pose. By distributing computations across many simple particles rather than one complex calculation, the system achieves accurate localization while keeping individual computational units simple and suitable for low-cost hardware
Solution Approach 2:
The patent changes parameters of the particle filter algorithm to reduce computational load: limiting the number of particles, using efficient importance sampling methods, and dynamically adjusting particle weights. These parameter optimizations maintain localization accuracy while reducing the processing power and memory capacity required
2Reliability
If complete SLAM algorithms are executed to maintain accurate robot pose updates, then localization reliability is improved, but processing time and energy consumption increase
Solution Approach 1:
The patent implements periodic execution of pose updates rather than continuous updates. The particle filter is executed at specific intervals or triggered by events such as significant sensor changes, which maintains localization reliability while reducing overall processing time and energy consumption compared to continuous algorithm execution
Solution Approach 2:
The patent performs preliminary actions by pre-computing particle weights and preparing probability distributions before actual pose estimation is needed. This allows the system to quickly update robot pose when required, improving response time while maintaining reliability through advance preparation of computational data
3Measurement precision
If full SLAM processing is performed to ensure accurate map generation, then mapping accuracy is improved, but memory capacity requirements increase
Solution Approach 1:
The patent extracts only the essential information needed for mapping from sensor data, storing only relevant features and observations rather than complete raw sensor streams. This selective extraction maintains mapping accuracy by preserving critical spatial relationships while significantly reducing memory capacity requirements
Solution Approach 2:
The patent uses a limited number of particles (partial action) rather than exhaustive sampling, which provides sufficient mapping accuracy for practical applications while consuming far less memory than complete or excessive sampling would require. The particle filter converges to accurate results with moderate particle counts, avoiding the need for large memory allocations
Data Source
AI summary
Techniques that optimize performance of simultaneous localization and mapping (SLAM) processes for mobile devices, typically a mobile robot. In one embodiment, erroneous particles are introduced to the particle filtering process of localization. Monitoring the weights of the erroneous particles relative to the particles maintained for SLAM provides a verification that the robot is localized and detection that it is no longer localized. In another embodiment, cell-based grid mapping of a mobile robot's environment also monitors cells for changes in their probability of occupancy. Cells with a changing occupancy probability are marked as dynamic and updating of such cells to the map is suspended or modified until their individual occupancy probabilities have stabilized. In another embodiment, mapping is suspended when it is determined that the device is acquiring data regarding its physical environment in such a way that use of the data for mapping will incorporate distortions into the map, as for example when the robotic device is tilted.