Robot Localization Variance Sampling for Sensor Reliability
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Solution Overview
Problem
Robots navigating and localizing in real environments face challenges such as sensor noise and drift, misleading features, and ambiguous geometry, which can lead to unreliable sensor data and loss of localization.
Innovation Solution
A system and process for determining sensor data reliability online, allowing robots to assess the quality of sensor data in real-time and provide user feedback for corrective action, thereby reducing the need for expensive offline systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If robots use sensor data for localization in real environments, then they can navigate autonomously, but sensor noise and drift cause unreliable localization
Solution Approach 1:
The system implements feedback by continuously monitoring sensor data quality metrics (variance, consistency) and using this information to dynamically adjust localization strategies. When sensor data quality degrades, the system detects this through statistical analysis and switches to alternative localization methods or requests user intervention, thereby maintaining reliable localization despite sensor noise and drift
Solution Approach 2:
The system dynamically adapts its localization approach based on real-time sensor data quality assessment. Instead of using a fixed localization method, it adjusts the weighting and selection of localization techniques according to current environmental conditions and sensor performance, enabling reliable autonomous navigation even when sensor conditions change
2Reliability
If expensive offline systems are used to ensure sensor data quality, then localization reliability improves, but system cost and complexity increase
Solution Approach 1:
The system performs self-service by automatically assessing its own sensor data quality using statistical metrics computed from the sensor data itself. It identifies unreliable data through variance analysis and consistency checks without requiring external validation systems, thereby maintaining reliability while avoiding the need for expensive offline verification infrastructure
Solution Approach 2:
The patent replaces complex mechanical or hardware-based quality assurance systems with computational methods. Instead of using expensive offline systems to verify sensor data, it employs algorithmic approaches (variance sampling, statistical consistency checks) to assess and ensure data quality, significantly reducing system cost and complexity while maintaining reliability
3Adaptability or versatility
If sensor data is collected in environments with poor quality features, then navigation coverage increases, but localization accuracy decreases
Solution Approach 1:
The system changes parameters by adjusting the statistical thresholds and quality metrics used to evaluate sensor data based on environmental conditions. In environments with poor features, it modifies its assessment criteria to account for higher expected variance, allowing it to operate in diverse environments while maintaining localization accuracy through adaptive parameter selection
Data Source
AI summary
A method of localizing a robot includes receiving odometry information plotting locations of the robot and sensor data of the environment about the robot. The method also includes obtaining a series of odometry information members, each including a respective odometry measurement at a respective time. The method also includes obtaining a series of sensor data members, each including a respective sensor measurement at the respective time. The method also includes, for each sensor data member of the series of sensor data members, (i) determining a localization of the robot at the respective time based on the respective sensor data, and (ii) determining an offset of the localization relative to the odometry measurement at the respective time. The method also includes determining whether a variance of the offsets determined for the localizations exceeds a threshold variance. When the variance among the offsets exceeds the threshold variance, a signal is generated.