Occupancy Grid Localization Using Mapping Sources for Large Grids
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Solution Overview
Problem
Grid-based Global Localization methods face exponential computational complexity, limiting their use to small grids due to the need for updating pose distributions over large grids, especially in environments with occlusions and noisy observations.
Innovation Solution
Transforming the Global Localization problem into a mapping problem by identifying border cells in the occupancy grid as 'mapping sources' that use sensor observations to update position grids, reducing complexity through Bayesian fusion and integer arithmetic for low computational power devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If grid-based Global Localization methods are used to determine robot pose in a known environment, then localization robustness is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent segments the global localization problem into multiple local localization problems by dividing the occupancy grid into smaller sub-grids. Each sub-grid is processed independently to compute local pose probabilities, which are then combined to obtain the global pose distribution. This segmentation reduces the exponential computational complexity of processing the entire grid at once while maintaining localization robustness through comprehensive coverage of all grid cells.
Solution Approach 2:
The patent transforms the computational problem by introducing a new dimension of processing - instead of computing pose probabilities across the entire grid simultaneously in a single exponential-time operation, the method processes the grid in a hierarchical manner across multiple levels (local sub-grids first, then combining results). This dimensional transformation of the computational approach converts the exponential complexity into a more manageable form while preserving the robustness benefits of grid-based methods.
2Measurement precision
If large grids are used for localization, then measurement precision and coverage are improved, but computational cost increases exponentially
Solution Approach 1:
The patent divides large occupancy grids into smaller sub-grids that can be processed independently and in parallel. This segmentation allows the system to maintain high measurement precision by considering all grid cells for comprehensive pose estimation, while simultaneously improving computational efficiency by processing smaller sub-grids separately and combining their results, avoiding the exponential complexity of processing the entire large grid at once.
3Measurement precision
If complete occupancy grid updates are performed, then localization accuracy is maintained, but computational power requirements become prohibitive for embedded systems
Solution Approach 1:
The patent segments the computationally intensive grid update process into smaller sub-grid operations that can be executed with limited computational resources. By processing local sub-grids independently and combining their results, the system maintains localization accuracy through comprehensive pose probability computation while reducing energy consumption to levels suitable for embedded systems with limited processing power and battery capacity.
Data Source
AI summary
A method of localizing a mobile body (MB) in a known environment, includes the following steps: a) defining an occupancy grid (G) modeling the environment; b) defining a set of position grids (Π) each position grid being associated to a heading of the mobile body; c) receiving a time series of measurements (z1, z2, . . . ) from a distance sensor carried by the mobile body; and d) upon receiving a measurement of the time series, updating the pose probabilities of the position grids as a function of present values of the occupancy probabilities and of the received measurement; wherein step d) is carried out by applying an inverse sensor model to the received measurement, while considering the distance sensor co-located with a detected obstacle and by applying Bayesian fusion to update the pose probabilities of the position grids.