Camera Pose Estimation With Moving Object Smoothness Constraint
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera pose estimation methods in autonomous vehicle navigation are prone to errors due to factors like host vehicle movement, road vibrations, and calibration issues, leading to unreliable estimates of vehicle and obstacle positions, which can be detrimental for safe navigation in urban environments.
Innovation Solution
Incorporating a moving object smoothness constraint into the camera pose estimation objective function, based on object detection and tracking results, to regularize the estimation process and minimize perturbations, thereby improving the robustness of camera pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera pose estimation methods are used, then the estimation process is simple, but the accuracy and reliability of vehicle position and orientation estimates deteriorate due to errors from vehicle movement, road vibrations, and calibration issues
Solution Approach 1:
The patent introduces an intermediary constraint function based on moving object detection and tracking to mediate between the camera pose estimation and the objective function. This constraint function acts as a mediator that incorporates temporal continuity information from tracked objects, thereby improving estimation accuracy without directly modifying the core pose estimation algorithm structure
Solution Approach 2:
The patent modifies the objective function by adding a constraint term that changes the parameter space of the optimization problem. The constraint function incorporates temporal information from previous frames through object tracking, effectively changing the parameters used in pose estimation to include temporal continuity constraints, which improves accuracy while managing complexity through structured parameter expansion
2Reliability
If a constraint function based on object detection and tracking is incorporated, then the robustness of camera pose estimation improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary object detection and tracking in previous frames before the final pose estimation. By pre-processing and tracking objects in advance, the system prepares constraint information that can be directly applied to the objective function, reducing the computational burden during real-time pose estimation while maintaining improved robustness
Solution Approach 2:
The patent maintains continuous tracking of moving objects across multiple frames, creating a continuous stream of constraint information. This continuity allows the system to leverage temporal information from previous frames to constrain the pose estimation, improving reliability by using ongoing tracking data rather than discrete, isolated measurements
Data Source
AI summary
Disclosed are devices, systems and methods for incorporating a smoothness constraint for camera pose estimation. One method for robust camera pose estimation includes determining a first bounding box based on a previous frame, determining a second bounding box based on a current frame that is temporally subsequent to the previous frame, estimating the camera pose by minimizing a weighted sum of a camera pose function and a constraint function, where the camera pose function tracks a position and an orientation of the camera in time, and where the constraint function is based on coordinates of the first bounding box and coordinates of the second bounding box, and using the camera pose for navigating the vehicle. The method may further include generating an initial estimate of the camera pose is based on a Global Positioning System (GPS) sensor or an Inertial Measurement Unit (IMU).