Camera Extrinsic Self-Calibration Using Scale-Aware Depth and Ego-Motion
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Solution Overview
Problem
Existing camera calibration methods are labor-intensive, require manual target image capture, 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 that uses image sequences to infer camera poses without external 3D supervision, combining view synthesis, photometric consistency, and temporal and cross-camera constraints to estimate extrinsic parameters, utilizing pretrained scale-aware depth networks and a curriculum learning strategy to calibrate multiple cameras simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual target image capture and strong scene assumptions are used for camera calibration, then calibration accuracy may be improved in controlled settings, but labor intensity and inapplicability to unstructured environments increase
Solution Approach 1:
The system performs self-calibration using only image sequences from the cameras themselves, without requiring manual capture of target images or external supervision. The calibration process is autonomous, using the cameras' own output to infer extrinsic parameters through self-supervised learning with photometric consistency and temporal constraints.
Solution Approach 2:
The patent replaces manual mechanical calibration processes with an automated computational approach. Instead of physically positioning target images and manually adjusting camera parameters, the system uses algorithmic processing of image sequences to automatically infer calibration parameters, substituting mechanical/manual operations with computational automation.
2Ease of manufacture
If manual target image capture and strong scene assumptions are used for camera calibration, then calibration process may be simplified in controlled settings, but applicability to unstructured environments deteriorates
Solution Approach 1:
The calibration method is designed to be universally applicable across different environments and camera configurations. It works with multiple camera types (pinhole, unified, extended unified, double sphere models) and operates in unstructured environments without requiring controlled settings or specific scene assumptions, making the system adaptable and versatile.
Solution Approach 2:
The system dynamically adapts to different environmental conditions and camera configurations without requiring manual reconfiguration. The self-supervised learning approach automatically adjusts to varying terrains and unstructured environments, making the calibration process flexible and dynamically adaptable rather than static and environment-specific.
3Measurement precision
If external 3D supervision is used for camera calibration, then calibration accuracy may be improved, but system complexity and cost increase
Solution Approach 1:
The system eliminates the need for external 3D sensors or supervision by using only the cameras' own image sequences for calibration. The self-supervised learning framework enables cameras to calibrate themselves without additional hardware, reducing system complexity while maintaining calibration accuracy through photometric consistency and temporal constraints.
Solution Approach 2:
The patent extracts the calibration capability from the camera system itself, removing the dependency on external 3D supervision infrastructure. By extracting and utilizing only the image data already captured by the cameras, the system eliminates the need for additional sensors, reducing hardware complexity and system cost while maintaining calibration functionality.
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.