Sensor Calibration for Dynamic Vehicle Navigation
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Solution Overview
Problem
Calibration of imaging devices mounted on vehicles is time-consuming and burdensome due to frequent recalibration needs caused by vehicle movements and environmental factors, which is not effectively addressed by traditional methods suitable for stationary environments.
Innovation Solution
A depth-resolving system that includes a LIDAR device and a camera, where a computer determines calibration parameters by minimizing a cost function evaluating the distance between depth-motion and optical-motion vectors, allowing for on-the-fly calibration without fixed targets, thereby improving the resolution and reducing artifacts in depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional calibration methods are used for stationary imaging devices, then calibration precision is maintained, but calibration time and operational burden increase significantly when devices are mounted on moving vehicles
Solution Approach 1:
The system performs self-calibration by automatically comparing depth data from the first sensor with optical data from the second sensor, eliminating the need for manual operation. The computer executes calibration algorithms that process sensor data and adjust parameters autonomously, allowing the system to calibrate itself during normal operation without human intervention.
Solution Approach 2:
The calibration system transitions from static, stationary calibration to dynamic calibration suitable for moving vehicles. The system continuously processes depth-motion vectors and optical-motion vectors during vehicle movement, enabling calibration to be performed on-the-fly rather than requiring stationary conditions and manual intervention.
2Measurement precision
If frequent recalibration is performed to maintain accuracy on moving vehicles, then measurement precision is improved, but productivity and operational efficiency deteriorate due to time consumption
Solution Approach 1:
The calibration process becomes continuous rather than periodic or manual. The computer continuously compares depth-motion vectors with optical-motion vectors and adjusts calibration parameters in real-time during vehicle operation, maintaining measurement precision without interrupting productivity or requiring separate calibration sessions.
Solution Approach 2:
The manual mechanical calibration process is replaced with an automated computational system. The computer executes algorithms that process sensor data, calculate calibration parameters, and adjust sensor alignment automatically, replacing the need for manual mechanical adjustment and significantly improving operational efficiency while maintaining precision.
3Reliability
If manual calibration to stationary targets is used, then calibration reliability is achieved, but adaptability to moving vehicle environments and real-time operation deteriorates
Solution Approach 1:
The calibration system adapts to dynamic vehicle environments by processing motion vectors during movement. The system calculates depth-motion vectors from the first sensor and optical-motion vectors from the second sensor, enabling calibration to be performed reliably while the vehicle is in motion rather than requiring stationary conditions.
Solution Approach 2:
The calibration system becomes universal by working in both stationary and moving environments. The computer-executed algorithms can process sensor data regardless of vehicle motion state, making the calibration system adaptable to various operational conditions including real-time vehicle operation, thereby improving versatility while maintaining reliability.
Data Source
AI summary
A system and methods are described for calibrating one sensor with respect to another. A method includes: determining a depth-motion vector using a first sensor; determining an optical-motion vector using a second sensor; and calibrating the first sensor with respect to the second sensor by minimizing a cost function that evaluates a distance between the depth-motion and optical-motion vectors.