Camera Pose Estimation Using Dynamic Object Homography
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Solution Overview
Problem
Existing autonomous vehicle systems face challenges in accurately estimating the rotation of cameras mounted on vehicles, which affects the precision of object detection and navigation, particularly due to limitations in using static objects like lane markings, which can be occluded or error-prone.
Innovation Solution
A method that utilizes dynamic objects, such as vehicles traveling in the same direction and at a safe distance, to estimate camera pose by applying a homography model that corrects for relative motion, enhancing the accuracy of camera rotation estimation and overcoming limitations of static object-based methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static objects like lane markings are used for camera pose estimation, then the method is simple to implement, but the accuracy deteriorates due to occlusion and error-prone detections
Solution Approach 1:
The patent combines static objects (lane markings) and dynamic objects (other vehicles) into a unified pose estimation framework. The system processes both static and dynamic object detections simultaneously, using a combined optimization approach that leverages the complementary strengths of both object types to achieve more accurate camera pose estimation while maintaining implementation feasibility.
Solution Approach 2:
The patent introduces dynamic objects with known motion models into the pose estimation process. By utilizing the predictable motion patterns of other vehicles, the system can compensate for occlusions and errors in static object detections, thereby improving measurement precision without significantly increasing implementation complexity.
2Measurement precision
If dynamic objects are used for camera pose estimation, then the accuracy and robustness improve, but the device complexity increases
Solution Approach 1:
The patent employs dynamic objects with known motion models to enhance pose estimation accuracy. By incorporating the predictable motion patterns of other vehicles into the optimization framework, the system achieves more robust camera pose estimation while managing complexity through structured integration of dynamic constraints.
Solution Approach 2:
The system uses feedback from dynamic object motion models to refine camera pose estimates. The known motion patterns of other vehicles provide constraining feedback that helps disambiguate pose solutions, particularly in scenarios where static objects are occluded or insufficient, thereby improving accuracy without proportionally increasing system complexity.
3Productivity
If static objects are used for pose constraints, then the computation is simpler, but the visible range and reliability are limited
Solution Approach 1:
The patent merges static and dynamic object constraints into a unified optimization framework. This combination allows the system to leverage the computational simplicity of static object processing while incorporating the enhanced reliability provided by dynamic object motion models, achieving both efficiency and improved reliability through integrated processing.
Solution Approach 2:
By incorporating dynamic objects with known motion models, the system extends its reliable measurement range beyond the limitations of static objects. The motion predictions from dynamic objects provide additional reliable constraints that improve pose estimation reliability, particularly in scenarios where static objects are occluded or unavailable.
Data Source
AI summary
System and method for using dynamic objects for camera pose estimation are disclosed. In one aspect, the method includes receiving a first image from a camera of an autonomous vehicle and acquiring first camera pose constraints based on one or more static objects detected in the first image. The method further includes receiving a second image from the camera and acquiring second camera pose constraints based on one or more static objects detected in the second image. The method further includes acquiring third camera pose constraints based on one or more dynamic objects detected in the first and the second image. The method finally includes an estimation of at least one pose of the camera that satisfies the first camera pose constraints, the second camera pose constraints, and the third camera pose constraints.