Vehicle Localization Error Estimation Using Multi-Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing localization systems for autonomous and semi-autonomous vehicles face challenges in accurately determining errors in vehicle pose, particularly due to noise, incomplete sensor data, and incomplete HD maps, which can lead to less relevant error estimates and lack of error bounds.
Innovation Solution
The system determines errors in vehicle localization by fusing different types of sensor data, including motion data, image data, LiDAR data, and RADAR data, to propagate and combine errors, providing a more accurate final error estimate that is continuously updated with new data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single type of sensor data is used to determine localization error, then the system complexity is reduced, but the measurement precision of the error estimate deteriorates
Solution Approach 1:
The patent combines multiple sensor data types (IMU motion data, camera image data, LiDAR point cloud data, RADAR data) to determine localization error. By merging data from these different sensors, the system achieves more precise error estimation than would be possible with a single sensor type, while managing the complexity through integrated processing architecture.
2Measurement precision
If error determination is performed continuously with new sensor data, then the measurement precision of error estimates is improved, but the use of energy increases
Solution Approach 1:
The system performs preliminary error determination using motion data from IMU sensors, which provides a baseline error estimate. This preliminary action allows the system to avoid more energy-intensive processing of image, LiDAR, and RADAR data unless necessary, thereby reducing overall energy consumption while maintaining acceptable measurement precision.
Solution Approach 2:
The patent implements partial processing by selectively applying different levels of error determination based on operational conditions. The system uses motion data for continuous error tracking and supplements it with more intensive sensor fusion processing only when needed, achieving a balance between measurement precision and energy consumption.
3Ease of operation
If motion data alone is used to determine error, then the ease of operation is improved, but the reliability of the error determination deteriorates
Solution Approach 1:
The patent segments the error determination process into two independent components: motion-based error determination and feature-matching error determination. Each component can operate independently, maintaining ease of operation for each segment, while their combination through data fusion improves overall reliability by compensating for the limitations of individual methods.
4Measurement precision
If feature comparison between sensor data and map is performed, then the measurement precision of localization is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary error determination module that processes the comparison between sensor data features and map features. This intermediary component manages the complexity of feature matching by providing a standardized interface between the sensors and the localization system, thereby improving localization precision while controlling device complexity through modular architecture.
Data Source
AI summary
In various examples, accuracy determinations for localization in autonomous and semi-autonomous systems and applications are described herein. Systems and methods are disclosed that determine one or more errors associated with vehicle localization using various types of sensor data generated using a vehicle. For instance, a first component of the vehicle may use a map and first sensor data to determine an estimated pose of the vehicle. A second component of the vehicle may then determine the error(s) associated with the estimated pose based on both actual motion of the vehicle within the environment, as determined using second sensor data, and comparing features represented by the first sensor data to features represented by the map. In some examples, the second component may further determine information associated with the error(s), such as one or more uncertainties associated with the error(s).