Sensor Calibration via Semantic Segmentation of Invariant Objects
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Solution Overview
Problem
Current sensor calibration techniques require infrastructure and human operators, leading to undesirable downtime, potential safety risks, and inefficiencies, especially in autonomous vehicles where precise calibration is critical for safe navigation.
Innovation Solution
The use of semantic segmentation information to verify and calibrate sensors without infrastructure, by comparing features in sensor data to invariant objects, allowing for real-time identification and correction of calibration errors, enabling infrastructure-free and automated calibration processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If infrastructure-based calibration techniques are used, then measurement precision is improved, but loss of time increases due to bringing the system to calibration locations
Solution Approach 1:
The system performs self-calibration by automatically detecting invariant objects in the environment and using them as reference points. The calibration process is autonomous, requiring no human operators or external infrastructure, thereby eliminating downtime while maintaining precision through automated feature matching and transformation calculations.
Solution Approach 2:
The patent extracts the calibration function from external infrastructure and relocates it to the sensor system itself. By removing the dependency on fiducial markers and external calibration devices, the system can perform calibration anywhere in the environment using naturally occurring invariant objects, thus eliminating the need to transport the system to specific calibration locations.
2Measurement precision
If infrastructure-based calibration techniques are used, then measurement precision is improved, but device complexity increases due to requiring fiducial markers and calibration equipment
Solution Approach 1:
The patent removes the need for external calibration infrastructure by extracting the reference functionality from physical fiducial markers and embedding it in the software through detection of invariant objects. This eliminates complex calibration equipment while maintaining precision through algorithmic identification of stable environmental features.
Solution Approach 2:
The patent replaces mechanical calibration infrastructure (fiducial markers, calibration devices) with a computational approach using image processing and feature matching algorithms. The mechanical system of physical reference objects is substituted with a software-based system that detects and utilizes invariant objects in the environment, thereby reducing device complexity.
3Measurement precision
If manual calibration with human operators is used, then measurement precision is improved, but productivity decreases due to manual process slowness
Solution Approach 1:
The system performs automated self-calibration without human operators by detecting invariant objects, extracting features, and computing transformation parameters automatically. This autonomous process maintains precision through rigorous feature matching while dramatically increasing productivity by eliminating manual intervention and reducing calibration time.
Solution Approach 2:
The system implements automated feedback loops where sensor data is continuously analyzed, calibration accuracy is verified through invariant object detection, and adjustments are made automatically. This closed-loop approach maintains measurement precision while increasing productivity through rapid iterative calibration without manual oversight.
Data Source
AI summary
This disclosure is directed to validating a calibration of and/or calibrating sensors using semantic segmentation information about an environment. For example, the semantic segmentation information can identify bounds of objects, such as invariant objects, in the environment. Techniques described herein may determine sensor data associated with the invariant objects and compare that data to a feature known from the invariant object. Misalignment of sensor data with the known feature can be indicative of a calibration error. In some implementations, the calibration error can be determined as a distance between the sensor data and a line or plane representing a portion of the invariant object.