Sensor Uncertainty Mapping for Reliable Robot State Estimation
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Solution Overview
Problem
Mobile robots face uncertainty in position, orientation, and velocity estimation due to sensor limitations, environmental complexity, and unmodeled physical effects, leading to inaccurate navigation and decision-making.
Innovation Solution
A system and method for generating confident zone maps using sensor data to categorize regions into different confidence levels based on error ranges, leveraging multiple sensor modalities to improve state estimation and navigation precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is used for state estimation, then position and orientation information is obtained, but measurement uncertainty and error arise due to sensor limitations
Solution Approach 1:
The patent combines multiple sensor modalities (e.g., visual, inertial, wheel encoders) to create a fused state estimation system. By merging data from different sensors with complementary strengths, the system achieves more reliable position and orientation estimates than any single sensor could provide alone, thereby resolving the contradiction between measurement precision and reliability.
Solution Approach 2:
The system implements feedback mechanisms where state estimation results and their associated uncertainties are continuously monitored and used to adjust sensor fusion weights and navigation decisions. This feedback loop allows the system to adapt to varying sensor reliability conditions and maintain accurate state estimation despite individual sensor limitations.
2Reliability
If multiple sensor modalities are used to improve state estimation, then measurement reliability increases, but system complexity increases
Solution Approach 1:
The patent segments the sensor system into distinct modalities (visual sensors, inertial sensors, wheel encoders) with dedicated processing pipelines for each. This segmentation allows independent optimization and calibration of each sensor type while maintaining overall system reliability, managing complexity through modular architecture.
Solution Approach 2:
The sensor fusion framework is designed to be universal and modifiable, allowing different sensor modalities to be added or removed based on specific application needs. The core architecture handles multiple sensor types through a unified state estimation approach, reducing complexity by providing a single framework that accommodates various sensor configurations.
3Measurement precision
If confident zone maps are generated for all sensor modalities, then navigation accuracy improves, but data processing time increases
Solution Approach 1:
The system pre-generates confident zone maps for each sensor modality during periods when navigation decisions are not time-critical. These pre-computed confidence maps are stored and rapidly retrieved during actual navigation, allowing accurate navigation decisions without real-time computation delays, thus resolving the contradiction between precision and processing time.
Solution Approach 2:
Rather than computing confident zone maps for all sensor modalities simultaneously at full resolution, the system computes maps selectively based on current navigation needs and sensor reliability assessments. This partial computation approach reduces processing time while maintaining sufficient navigation accuracy by focusing computational resources on the most relevant sensor modalities and regions.
Data Source
AI summary
A computer-implemented system and method include generating a set of state data using sensor data of a particular sensor modality at a set of locations in a region. Each state data includes a corresponding position estimate of a vehicle. A set of contour ranges is generated. Each contour range is indicative of a respective error range of given state data with respect to corresponding ground truth data for a given location. The region is categorized into at least (i) a first confident level associated with a first error range and (ii) a second confident level associated with a second error range. A first confident zone corresponds to locations associated with the first confident level. A second confident zone corresponds to locations associated with the second confident level. A confident zone map includes at least the first confident zone and the second confident zone.