Semantic Object Calibration for Multi-Sensor Vehicle Localization
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Solution Overview
Problem
Existing vehicle navigation systems face challenges in accurately determining vehicle location and calibrating sensors, leading to inconsistent data fusion, segmentation, and tracking issues due to misalignment or incorrect calibration of sensors.
Innovation Solution
The system uses semantic map data to project and match objects in sensor data, determining calibration parameters based on epipolar geometry, and predicts sensor performance to schedule predictive maintenance, ensuring accurate localization and calibration across overlapping and non-overlapping sensor fields of view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are calibrated using traditional methods, then calibration can be performed, but measurement precision and reliability are insufficient due to misalignment and incorrect calibration
Solution Approach 1:
The patent introduces semantic map data as an intermediary element to bridge sensor data from different sources. By projecting semantic map objects into sensor data and using them as reference points, the system establishes a common coordinate framework that enables accurate calibration across multiple sensors without direct pairwise alignment, resolving the measurement precision and reliability contradiction.
Solution Approach 2:
The system implements feedback by continuously comparing sensor measurements with semantic map data and using the discrepancies to iteratively refine calibration parameters. This closed-loop approach ensures that calibration accuracy improves over time and maintains high reliability through ongoing validation against the semantic map reference framework.
2Adaptability or versatility
If multiple sensors with overlapping and non-overlapping fields of view are used, then coverage is improved, but device complexity increases making calibration difficult
Solution Approach 1:
The patent creates a universal calibration framework based on semantic map data that works across all sensor types and configurations, whether they have overlapping or non-overlapping fields of view. The semantic map serves as a common reference that unifies the calibration process, eliminating the need for complex sensor-specific calibration procedures and enabling consistent multi-sensor operation.
Solution Approach 2:
Semantic map data acts as an intermediary that connects sensors with non-overlapping fields of view, allowing calibration without direct visual overlap. By projecting semantic objects into each sensor's view and using these projections as reference points, the system enables calibration across the entire sensor array regardless of geometric relationships between individual sensors.
3Productivity
If traditional data fusion methods are used, then processing can be performed, but tracking accuracy deteriorates due to misalignment issues
Solution Approach 1:
The patent uses semantic map data as an intermediary reference framework that enables accurate data fusion by providing a common coordinate system. All sensor data is transformed and fused relative to the semantic map projections, ensuring that objects tracked across multiple sensors maintain consistent positions and trajectories, thereby resolving misalignment issues and improving tracking accuracy.
Solution Approach 2:
The system dynamically adjusts calibration parameters based on comparisons between sensor data and semantic map projections. By continuously optimizing transformation parameters (rotation, translation, scaling) that map sensor coordinates to semantic map coordinates, the system maintains high tracking accuracy even as sensors move or environmental conditions change.
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.