Doppler Sensor Offset Calibration Using Static Surface Motion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for calibrating doppler-based sensors on autonomous vehicles struggle to accurately adjust for rotational and linear offsets in dynamic and feature-poor environments without relying on specific environmental features or dedicated calibration cycles.
Innovation Solution
A method for calibrating relative positions of optical sensors on a mobile platform by deriving absolute motions from frames captured by these sensors, using static reference surfaces to calculate rotational and linear offsets in real-time, even in unknown or dynamic environments, through joint optimization of observed motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used, then calibration accuracy is improved, but the system requires dedicated calibration cycles and specific environmental features
Solution Approach 1:
The system performs self-calibration by automatically detecting static reference surfaces in the environment and using them to compute sensor offsets. The calibration process requires no external intervention, dedicated calibration cycles, or special calibration equipment - the system uses its own sensor data to adjust its parameters in real-time during normal operation.
Solution Approach 2:
The calibration method works universally across different environments without requiring specific environmental features. Any static reference surface (buildings, ground, objects) can be used for calibration, making the system adaptable to diverse settings including feature-poor environments. The same calibration mechanism serves both calibration purposes and normal navigation operations.
2Adaptability or versatility
If real-time calibration is implemented, then adaptability to dynamic conditions is improved, but computational requirements and processing time increase
Solution Approach 1:
The calibration process continues uninterrupted during normal system operation. Instead of performing calibration in separate dedicated cycles, the system continuously processes calibration data alongside navigation data, updating sensor offsets in real-time as the vehicle moves through the environment. This ensures calibration remains current without stopping normal operations.
Solution Approach 2:
The system performs calibration computations partially in the background during normal operations, using available computational resources efficiently. By processing calibration updates incrementally rather than requiring complete recalibration cycles, the system achieves real-time adaptability without excessive processing time or computational burden.
3Measurement precision
If calibration relies on static reference surfaces, then measurement precision is improved, but the method fails in feature-poor environments
Solution Approach 1:
The system introduces a virtual reference frame as an intermediary that can be established through multiple static reference surfaces detected in the environment. Instead of requiring a single predetermined calibration target, the system uses any detected static surface (buildings, ground, objects) as an intermediary to infer sensor offsets, enabling calibration in diverse environments including feature-poor areas.
Solution Approach 2:
The calibration system dynamically adapts to the available environmental features by detecting and utilizing any static reference surfaces present in the current environment. Rather than requiring a specific predetermined calibration target, the system flexibly identifies suitable reference surfaces as it moves through different locations, enabling it to operate effectively in both feature-rich and feature-poor environments.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time milli-radian rotational calibration and compensation for changes in temperature and sensor mounting fatigue, ensuring accurate sensor alignment and improved navigation in dynamic conditions.
Implementation Method 1
a first set of points containing radial positions, azimuthal positions, radial distances, and radial velocities relative to a first field of view of the first optical sensor
Data Source
AI summary
A method includes: deriving a first absolute motion of the first optical sensor based on radial positions, azimuthal positions, radial distances, and radial velocities of points in a first cluster of points representing a first static reference surface in a first frame captured by the first optical sensor; deriving a second absolute motion of the second optical sensor based on radial positions, azimuthal positions, radial distances, and radial velocities of points in a first cluster of points representing a first static reference surface in a second frame captured by the second optical sensor; calculating a rotational offset between the first optical sensor and the second optical sensor based on the first absolute motion and the second absolute motion; and aligning a third frame captured by the first optical sensor with a fourth frame captured by the second optical sensor based on the rotational offset. a