In-situ Sensor Calibration Using TSDF Models
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Solution Overview
Problem
Manual sensor calibration in autonomous vehicles is tedious, time-consuming, and error-prone, requiring significant manual labor and dedicated facilities, and calibration parameters are often specific to individual sensors, limiting scalability.
Innovation Solution
An on-board system uses data from already calibrated sensors to calibrate other sensors through non-linear optimization techniques, building a Truncated Signed Distance Field (TSDF) model of the environment to determine accurate calibration parameters, allowing for automatic calibration in natural environments and reducing the need for manual calibration targets and facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sensor calibration is performed using dedicated facilities and calibration targets, then measurement precision is improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The system performs self-calibration by using its own sensor data to automatically determine calibration parameters without requiring external operators or specialized facilities. The calibration process is autonomous, eliminating manual intervention while maintaining accuracy through iterative optimization algorithms that use real-world sensor observations.
Solution Approach 2:
The patent extracts the calibration function from specialized dedicated facilities and calibration targets, enabling calibration to be performed in-situ using naturally available environmental features. This removes the dependency on external calibration infrastructure and allows calibration to occur in the actual operating environment.
2Measurement precision
If manual sensor calibration is performed meticulously, then measurement precision is improved, but device complexity and ease of operation worsen
Solution Approach 1:
The calibration system operates autonomously without requiring skilled operators to manually manipulate sensors or interpret calibration data. The system automatically collects sensor data, processes it through optimization algorithms, and determines calibration parameters, making the process as simple as deploying the sensors in the environment.
Solution Approach 2:
The patent replaces manual mechanical calibration operations with automated computational processes. Instead of physically positioning sensors and manually adjusting parameters, the system uses computer vision algorithms and optimization techniques to automatically determine calibration parameters from sensor data.
3Measurement precision
If traditional calibration methods are used for each individual sensor, then measurement precision is improved, but productivity and scalability deteriorate
Solution Approach 1:
The calibration system is designed to handle multiple sensor types and configurations using a unified approach. The same calibration pipeline can process data from various sensors (cameras, LIDAR, radar) and determine calibration parameters for multiple sensors simultaneously, making the system universally applicable across different sensor configurations without requiring separate calibration procedures for each sensor.
4Measurement precision
If calibration is performed in dedicated facilities, then measurement precision is improved, but loss of time and productivity worsen due to transportation and facility requirements
Solution Approach 1:
The patent extracts the calibration process from dedicated facilities and transports it to the actual deployment environment. By using in-situ calibration with naturally available environmental features, the system eliminates transportation time and facility setup time, performing calibration directly where the sensors will operate.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using naturally collected data in sensor calibration. One of the methods includes obtaining, using a first, calibrated sensor, a first plurality of raw sensor measurements of an environment; determining, from the first plurality of raw sensor measurements, a Truncated Signed Distance Field (TSDF)-based model of surfaces in the environment; obtaining, using a second sensor, a second plurality of raw sensor measurements of the environment; determining a multi-dimensional point cloud representation of the environment; and determining refined values of the set of calibration parameters of the second sensor based on a difference between, for each data point, (i) the multiple values that define the data point and (ii) multiple values that define a target data point derived from the TSDF-based model of surfaces in the environment.