Relative Sensor Orientation Calibration via 3D Box Projection
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Solution Overview
Problem
Existing methods for calibrating the relative orientation of sensors, such as LIDAR and cameras, in autonomous vehicles are inefficient and prone to errors, especially when there are small angular discrepancies, leading to significant offsets in projected bounding boxes and erroneous links between 2D and 3D annotations.
Innovation Solution
A method that groups sensor frames based on temporal correlation, projects 3D bounding boxes onto the image plane of an area sensor, calculates an energy term based on coordinate differences, and optimizes the relative sensor orientation by minimizing this energy term, using neural networks for high-quality annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used to determine sensor orientation, then the calibration process can be completed, but small angular errors lead to significant offsets in projected bounding boxes and reduce annotation quality
Solution Approach 1:
The patent implements an iterative optimization process where the energy term is calculated based on projection errors and used to update sensor orientation parameters. This feedback loop continues until convergence, allowing small angular errors to be corrected through repeated refinement of the calibration parameters, thereby improving both measurement precision and projection accuracy
Solution Approach 2:
The patent replaces traditional manual or geometry-based calibration methods with an energy-minimization approach. By defining an energy term that quantifies projection errors and using optimization algorithms to minimize this energy, the system substitutes mechanical/geometric calibration with a computational optimization process that is more robust to small angular errors
2Reliability
If manual quality checks are performed to ensure annotation quality, then annotation quality can be maintained, but the process becomes time-consuming and reduces productivity
Solution Approach 1:
The patent enables the calibration system to self-correct by automatically detecting projection errors through the energy term and iteratively optimizing sensor orientation parameters without human intervention. This self-service capability eliminates the need for manual quality checks while maintaining high annotation quality, thereby resolving the contradiction between reliability and productivity
Solution Approach 2:
The energy term serves as an intermediary that automatically quantifies annotation quality issues. Instead of requiring manual inspection, the energy term mediates between the projected bounding boxes and ground truth annotations, automatically identifying and correcting errors through optimization, thus maintaining quality while enabling automated high-throughput processing
3Manufacturing precision
If calibration correction is performed to eliminate offsets, then projection accuracy improves, but computational effort increases
Solution Approach 1:
The patent applies partial action by focusing computational effort only on optimizing the specific sensor orientation parameters that affect projection accuracy, rather than recalibrating the entire sensor system. The iterative optimization continues for a limited number of steps or until convergence, achieving sufficient projection accuracy without excessive computational investment
Solution Approach 2:
The calibration optimization is applied locally to the specific sensor pairs and time points where projection errors are detected, rather than globally recalibrating all sensors throughout the dataset. This localized approach improves projection accuracy where needed while minimizing overall computational effort by avoiding unnecessary recalibration of already-accurate sensor configurations
Data Source
AI summary
A method for calibrating the relative orientation of a spatial sensor and an area sensor includes: receiving a plurality of sensor frames of the spatial sensor and the area sensor; grouping sensor frames of the spatial sensor with sensor frames of the area sensor based on temporal correlation; selecting at least one group of frames comprising a spatial sensor frame and an area sensor frame; receiving annotations for the sensor frames in the at least one selected group, wherein the annotations comprise bounding boxes for detected objects; projecting the corners of a three-dimensional bounding box in the spatial sensor frame to an image plane of the area sensor, to produce a projected rectangle; calculating an energy term based on the coordinates of the corners of the projected rectangle and the corresponding bounding box in the area sensor frame; and optimizing the relative sensor orientation by minimizing the energy term.