Vehicle Sensor Feature Registration for Online Alignment
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Solution Overview
Problem
Existing sensor calibration methods for vehicles are time-consuming and require manual adjustment at service locations, failing to accurately adapt to changes in sensor position due to factors like bumps or weather, leading to inaccurate measurements.
Innovation Solution
A computer-implemented method for dynamically aligning sensors using feature extraction and registration from static objects, such as traffic signs and light poles, without relying on IMU/GPS, and utilizing on-board computing to continuously calibrate sensor parameters as the vehicle is driven.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration at service locations is performed, then sensor alignment accuracy is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs self-calibration by automatically detecting static objects in the environment and computing transformation parameters without human intervention. The sensor system itself carries out the calibration process by comparing observed object positions with predicted positions based on vehicle motion, eliminating the need for manual calibration services.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with an automated computational system. Instead of physically adjusting sensors at service locations, the system uses computer vision and coordinate transformation algorithms to automatically compute and apply calibration parameters based on environmental feature detection.
2Measurement precision
If manual calibration at service locations is performed, then sensor alignment accuracy is improved, but adaptability to real-time changes deteriorates
Solution Approach 1:
The calibration system transitions from a static, one-time manual calibration to a dynamic, continuous self-calibration process. The system continuously detects environmental features and updates transformation parameters in real-time as the vehicle moves, adapting to changing sensor positions caused by bumps, weather, or other factors during normal operation.
Solution Approach 2:
Instead of performing calibration only once at service locations, the system continuously performs calibration operations throughout vehicle operation. The useful action of calibration is maintained continuously by constantly monitoring environmental features and updating sensor alignment parameters, ensuring ongoing accuracy without interruption to vehicle use.
3Extent of automation
If feature registration with static objects is used, then real-time calibration capability is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential features needed for calibration from the complex sensor data stream. By focusing on detecting static environmental objects and their geometric features rather than processing all sensor information, the system reduces computational complexity while maintaining automatic calibration capability.
Solution Approach 2:
The patent introduces environmental static objects as intermediaries to facilitate calibration. These objects serve as reference markers that mediate between the sensor system and the vehicle coordinate system, simplifying the computational process by providing stable, easily detectable features for transformation calculations.
4Reliability
If continuous online calibration is performed, then measurement accuracy under varying conditions is improved, but processing time per calibration cycle increases
Solution Approach 1:
The system performs preliminary detection and identification of static environmental features before they are needed for calibration calculations. By pre-processing and storing feature information from the environment, the system reduces the time required for actual calibration computations when sensor realignment is detected, maintaining both reliability and efficiency.
Data Source
AI summary
A computer-implemented method for aligning a sensor to a vehicle includes receiving a first frame of measurement from the sensor which includes a first point cloud. One or more clusters Ci representing one or more objects or the ground are segmented. A first set of feature vectors fi is computed for each cluster Ci. Based on the first set of feature vectors fi a second set of feature vectors fi′ is predicted respectively using an initial transformation. A third set of feature vectors fj is computed for a second frame with a second point cloud with clusters Cj. A pair of matching clusters is identified from Ci and Cj. A feature distance between the matching clusters is computed. An alignment transformation is computed by updating the initial transformation based on the feature distance. The method further includes aligning the sensor and the vehicle based on the alignment transformation.