Semantic Map Object Localization for Vehicle Sensor Calibration
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Solution Overview
Problem
Current vehicle navigation systems face challenges in accurately determining vehicle location and sensor calibration, particularly in environments with complex semantic objects, which can lead to inconsistent sensor data and reduced system performance.
Innovation Solution
The use of semantic map data to project and match objects in sensor data, determining calibration parameters based on epipolar geometry, and predicting sensor performance to schedule predictive maintenance, thereby ensuring accurate localization and calibration of sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are calibrated to provide accurate input to vehicle computing systems, then localization accuracy is improved, but sensor calibration complexity and time consumption increase
Solution Approach 1:
The system performs preliminary calibration by capturing sensor data of semantic objects (traffic lights, signs, poles, lane markings) and storing their expected locations in map data before actual vehicle operation. This pre-established reference data enables faster localization during runtime by directly comparing sensor observations against pre-calibrated map information, reducing on-the-spot calibration time while maintaining accuracy
Solution Approach 2:
The system creates a digital copy of the physical environment through map data that contains projected locations of semantic objects. This copy is used for comparison with actual sensor data to determine vehicle location and detect calibration errors, eliminating the need for repeated physical calibration procedures while preserving measurement precision
2Measurement precision
If semantic map data is used to project and match objects in sensor data, then localization accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the environment into discrete semantic objects (traffic lights, signs, poles, lane markings) with specific classifications. Each object type has predefined characteristics and expected locations in map data. This segmentation allows the computing system to process and match individual object types separately rather than handling all visual data as a single complex task, reducing overall computational burden while improving localization precision
Solution Approach 2:
The system changes the parameter representation of environmental features by converting physical objects into classified semantic categories with specific attributes (type, location, covariance data). This parameter transformation enables efficient database queries and pattern matching algorithms that are computationally lighter than raw image processing, achieving high localization accuracy through structured data comparison
3Reliability
If multiple sensors are used to capture sensor data, then system robustness is improved, but sensor calibration difficulty increases
Solution Approach 1:
The system introduces map data containing projected semantic object locations as an intermediary reference framework between multiple sensors. Each sensor captures data of the same semantic objects, and the map data provides the ground truth locations for comparison. This intermediary enables automated calibration verification by checking whether sensor-measured object locations match the pre-stored map data, simplifying multi-sensor calibration while enhancing system robustness through redundant sensing
4Measurement precision
If sensor calibration is performed frequently to maintain accuracy, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system performs self-calibration verification by automatically comparing sensor-captured semantic object locations against pre-stored map data. The vehicle computing system independently detects calibration errors and determines corrective actions without requiring external calibration equipment or manual intervention. This self-service capability maintains high measurement precision while minimizing interruptions to vehicle operation, preserving productivity
Solution Approach 2:
The system implements continuous feedback by comparing real-time sensor observations of semantic objects with expected locations from map data. When discrepancies exceed threshold values, the system generates calibration error indicators and triggers corrective calibration procedures. This feedback mechanism ensures calibration accuracy is maintained only when necessary, avoiding unnecessary calibration operations that would reduce vehicle operational productivity
Data Source
AI summary
Techniques for determining a location of a vehicle in an environment using sensors and determining calibration information associated with the sensors are discussed herein. A vehicle can use map data to traverse an environment. The map data can include semantic map objects such as traffic lights, lane markings, etc. The vehicle can use a sensor, such as an image sensor, to capture sensor data. Semantic map objects can be projected into the sensor data and matched with object(s) in the sensor data. Such semantic objects can be represented as a center point and covariance data. A distance or likelihood associated with the projected semantic map object and the sensed object can be optimized to determine a location of the vehicle. Sensed objects can be determined to be the same based on matching with the semantic map object. Epipolar geometry can be used to determine if sensors are capturing consistent data.