Multi-Camera Extrinsic Self-Calibration From Depth and Ego-Motion
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Solution Overview
Problem
Existing camera calibration methods are labor-intensive, require manual tuning, and rely on strong assumptions about the scene, limiting their accuracy and applicability in unstructured environments, especially in autonomous vehicles and robots operating over varying terrains.
Innovation Solution
A self-supervised learning approach using scale-aware depth and ego-motion networks to estimate extrinsic camera parameters from unlabeled image sequences, eliminating the need for manual labor and ground-truth data, and enabling simultaneous calibration of multiple cameras without expensive optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual camera calibration methods are used, then calibration accuracy can be achieved under controlled conditions, but the process becomes labor-intensive and requires strong assumptions about the scene
Solution Approach 1:
The system performs self-calibration by automatically estimating extrinsic parameters from image sequences without manual intervention. The calibration process serves itself by using the camera's own captured images and instantaneous velocity data to compute calibration parameters, eliminating the need for manual tuning and ground-truth data
Solution Approach 2:
The patent replaces manual mechanical calibration processes with an automated computational system. Instead of physical adjustment and manual tuning, the system uses neural networks (depth network and pose network) to automatically estimate extrinsic parameters from image data and velocity information
2Measurement precision
If traditional calibration methods with ground-truth data are used, then accurate calibration can be achieved, but additional sensors and expensive optimization are required
Solution Approach 1:
The system uses only the camera's own captured images and its instantaneous velocity data to perform calibration. No additional sensors or ground-truth data are required - the camera calibrates itself using its native capabilities and publicly available velocity information from the autonomous system
Solution Approach 2:
The patent extracts calibration information directly from the image sequences and velocity data that are already being captured during normal operation. Instead of requiring separate calibration equipment or additional sensors, the system extracts the necessary calibration parameters from existing operational data
3Adaptability or versatility
If existing calibration methods are applied to unstructured environments, then some calibration can be achieved, but accuracy deteriorates due to strong assumptions about the scene
Solution Approach 1:
The system dynamically adapts to unstructured environments by using neural networks that learn from actual image sequences captured in the operating environment. Instead of relying on static assumptions about scene structure, the calibration process dynamically adjusts to the actual visual data and velocity measurements encountered during operation
Solution Approach 2:
The patent changes the calibration approach from assumption-based fixed parameters to data-driven learned parameters. The depth network and pose network learn appropriate parameters from actual image sequences and velocity data, allowing the system to adapt to varying terrains and unstructured environments while maintaining accuracy
4Measurement precision
If manual tuning and optimization processes are used, then calibration precision can be improved, but the calibration time and productivity are reduced
Solution Approach 1:
The patent replaces manual tuning and expensive optimization processes with automated neural network estimation. The depth network and pose network efficiently compute extrinsic parameters from image sequences and velocity data, achieving accurate calibration without the time-consuming manual intervention and optimization required by traditional methods
Data Source
AI summary
Systems and methods described herein relate to self-supervised scale-aware learning of camera extrinsic parameters. One embodiment processes instantaneous velocity between a target image and a context image captured by a first camera; jointly training a depth network and pose network based on scaling by the instantaneous velocity; produce depth map using the depth network; produce ego-motion of the first camera using the pose network; generate synthesized image from the target image using a reprojection operation based on the depth map, the ego-motion, the context image and camera intrinsics; determine photometric loss by comparing the synthesized image to the target image; generate photometric consistency constraint using a gradient from the photometric loss; determine pose consistency constraint between the first camera and a second camera; and optimize the photometric consistency constraint, the pose consistency constraint, the depth network and the pose network to generate estimated extrinsic parameters.