Autonomous Vehicle Landmark Localization With Pose-LIDAR Validation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately localizing themselves within their environment, particularly in situations where direct sensor data is not available, which can lead to errors in pose determination and potentially unsafe vehicle control.
Innovation Solution
A method that generates both LIDAR-based and pose-based predicted locations of landmarks without direct utilization of LIDAR data, using stored mappings and sensor data from non-vision sources like IMU and wheel encoders, and adjusts parameters based on error thresholds to validate and improve localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR-based predicted location is used for localization, then measurement precision is improved, but device complexity increases due to direct sensor data requirements
Solution Approach 1:
The patent introduces an intermediary system that generates pose-based predicted locations using stored mappings and sensor data from non-vision sources (IMU, wheel encoders) as a mediator between direct LIDAR sensing and localization determination. This intermediary approach allows the system to achieve accurate localization without directly processing complex LIDAR data, thereby reducing device complexity while maintaining measurement precision through the comparison of multiple predicted location sources.
2Measurement precision
If direct LIDAR data is utilized for pose determination, then localization accuracy is improved, but reliability decreases when sensor data is unavailable
Solution Approach 1:
The patent implements preliminary action by pre-storing detailed environmental mappings and pre-processing sensor calibration data before actual localization is needed. This allows the system to generate pose-based predicted locations using stored mappings and sensor data from non-vision sources when direct LIDAR data is unavailable, ensuring reliability continues under limited data conditions while maintaining accuracy through the pre-established reference framework.
Solution Approach 2:
The system dynamically changes operational parameters by switching between LIDAR-based and pose-based predicted location generation modes depending on data availability. When LIDAR data is unavailable, the system transitions to using stored mappings with sensor data from alternative sources, adjusting the localization approach parameters to maintain both reliability and accuracy across varying operational conditions.
3Measurement precision
If multiple predicted location methods are compared, then measurement precision is improved, but loss of time increases due to additional processing
Solution Approach 1:
The patent applies partial action by implementing selective comparison of predicted locations based on confidence thresholds and data availability rather than always performing full multi-method validation. The system compares LIDAR-based and pose-based predicted locations only when necessary and when confidence levels warrant it, reducing unnecessary processing time while maintaining high measurement precision through targeted validation of localization data.
Data Source
AI summary
Systems and methods for landmark-based localization of an autonomous vehicle (“AV”) are described herein. Implementations can generate a first predicted location of a landmark based on a pose instance of a pose of the AV and a stored location of the landmark, generate a second predicted location of the landmark relative to the AV based on an instance of LIDAR data, generate a correction instance based on the comparing, and use the correction instance in generating additional pose instance(s). Systems and methods for validating localization of a vehicle are also described herein. Implementations can obtain driving data from a past episode of locomotion of the vehicle, generate a pose-based predicted location of a landmark in an environment of the vehicle, and compare the pose-based predicted location to a stored location of the landmark in the environment of the vehicle to validate a pose instance of a pose of the vehicle.