Geo-Denied Localization Loop Error Mitigation for Automated Vehicles
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Solution Overview
Problem
Automated vehicles face navigation challenges when they lose access to real-time geolocation data, leading to errors in map localization and motion estimation due to the propagation of negative effects such as drift in sensor data outputs.
Innovation Solution
Implementing a geo-denied operational state that invokes a localization loop between a map localizer and a motion estimator, which uses LiDAR data and error detection and mitigation phases to maintain navigation without geolocation data, and applies covariance boost values to correct outlier measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a localization loop is invoked between map localizer and motion estimator to enable navigation without geolocation data, then the automated vehicle can continue navigating in geo-denied environments, but errors and drift in sensor data propagate over successive iterations leading to accumulated localization inaccuracies
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors localization outputs and motion estimation results from the localization loop. Error detection programming identifies when outputs exceed predefined thresholds, triggering mitigation actions. This closed-loop feedback prevents unbounded error propagation while maintaining navigation capability in geo-denied environments.
Solution Approach 2:
The system performs preliminary error detection and threshold checking on sensor data and localization outputs before these values can propagate through multiple iterations of the localization loop. By detecting potential errors in advance and applying covariance boosts proactively, the system prevents catastrophic feedback loops before they develop.
2Reliability
If error detection and mitigation programming is implemented to monitor and correct outliers in the localization loop, then catastrophic feedback loops are prevented and localization stability is improved, but system complexity and computational overhead increase
Solution Approach 1:
The patent dynamically adjusts the covariance matrix parameters of the motion estimator based on detected error conditions. When outliers are identified, the system modifies covariance values to reduce the influence of problematic sensor readings. This parameter adjustment approach provides adaptive error mitigation without requiring complete system redesign or complex additional hardware.
Solution Approach 2:
The error detection and mitigation programming acts as an intermediary layer between the sensor data input and the localization loop processing. This intermediate programming monitors data quality, detects anomalies, and applies corrections before data enters the main localization algorithm, isolating the complex error handling logic from the core navigation functions.
Data Source
AI summary
Embodiments described herein implement an improved autonomy system with a beneficial approach to implementation a localization loop. When the automated vehicle loses access to geolocation data updates, the autonomy system invokes a geo-denied localization loop that performs map localization and motion estimation functions without geolocation data. The localization loop feeds map localizer outputs into a motion estimator of the INS and/or the IMU, and feeds motion estimation outputs from the motion estimator back into the map localizer. When executing the localization loop, the autonomy system detects outlier measurements as errors in the map localizer and mitigates the errors in the map localizer or the motion estimator. The autonomy system executes programming in an error detection phase for monitoring and detecting errors in the localization loop, and an error mitigation phase for mitigating or resolving errors, such as applying a covariance boosting value on outputted data values.