Evidence Grid Navigation with Adjustable Sensor Parameters
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Solution Overview
Problem
Current navigation systems are limited in flexibility and accuracy, requiring fixed guide-paths, laser targets, or odometry, which are prone to errors and unable to adapt to changes in the environment, and 3D evidence grids are computationally burdensome and unreliable.
Innovation Solution
A navigation system using stereo range sensors to collect data, a data storage system with an evidence grid and adjustable sensor model parameters, and a grid engine to adjust parameters based on received data, allowing for dynamic mapping and navigation without pre-defined paths or static targets, and enabling real-time updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3-D evidence grids are used to represent the environment, then the data richness and navigation accuracy are improved, but the computational burden increases significantly
Solution Approach 1:
The patent segments the 3-D evidence grid into multiple 2-D evidence grids, each representing a different elevation level or layer of the environment. This segmentation reduces the computational complexity of processing the entire 3-D space at once while maintaining navigation accuracy by allowing independent processing of each 2-D layer.
Solution Approach 2:
The patent transforms the 3-D evidence grid problem into multiple 2-D evidence grid problems by introducing an elevation dimension as a separate indexing layer. This dimensionality change allows the system to leverage efficient 2-D grid processing algorithms while still representing three-dimensional space, thereby reducing computational burden.
2Productivity
If feature extraction is used to reduce computational burden, then the processing speed is improved, but the reliability decreases when guesses are wrong
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously compares sensor observations with the evidence grid model and adjusts the grid representation accordingly. This feedback loop allows the system to maintain high processing speed through efficient grid operations while ensuring reliability by correcting any inaccuracies through iterative refinement based on actual sensor data.
Solution Approach 2:
The evidence grid system performs self-correction and self-refinement by automatically updating its representation based on incoming sensor data without requiring external intervention. The system services itself by detecting discrepancies between the model and reality, then autonomously adjusting the grid to improve both speed and reliability simultaneously.
3Stability of the object's composition
If fixed guide-paths are used for navigation, then the system stability is improved, but the adaptability to environmental changes deteriorates
Solution Approach 1:
The patent transforms the static fixed guide-path system into a dynamic evidence grid system that can adapt its representation based on environmental changes. The grid structure provides stability through its organized spatial framework, while its content dynamically updates based on sensor input, allowing the system to maintain both stability and adaptability simultaneously.
Solution Approach 2:
The patent uses parameter changes in the evidence grid representation to adapt to environmental variations. By modifying the occupancy probabilities and confidence values in the grid cells based on new sensor data, the system can respond to environmental changes while maintaining the stable underlying grid structure for navigation planning.
Data Source
AI summary
Mapping, localization, and navigation systems for use with a mobile unit are provided that use a sensor model with adjustable parameters. The system includes range sensors configured to collect range data and a data storage system having stored therein an evidence grid representing an environment and a sensor model comprising adjustable parameters representing inaccuracies of the range sensors. A grid engine adjusts the adjustable parameters based on received range data from the range sensors. A navigation module can direct the mobile unit though the environment using the evidence grid, received range data, and adjustable parameters. The grid engine can also locate the mobile unit within the environment by using the adjusted sensor model parameters to compare the received range data against the evidence grid.