Overlapping and Non-overlapping Camera Layouts for Robot Pose Estimation
This work addresses robot pose estimation for robotics applications, but it is incremental as it compares existing camera layout approaches without introducing new methods.
The paper tackled the problem of estimating a robot's ego-motion by comparing overlapping (stereo pairs) and non-overlapping (individual cameras) camera layouts, finding that stereo systems offer higher accuracy but require more computation, while non-overlapping layouts provide a larger field of view but face scale ambiguity issues.
We study the use of overlapping and non-overlapping camera layouts in estimating the ego-motion of a moving robot. To estimate the location and orientation of the robot, we investigate using four cameras as non-overlapping individuals, and as two stereo pairs. The pros and cons of the two approaches are elucidated. The cameras work independently and can have larger field of view in the non-overlapping layout. However, a scale factor ambiguity should be dealt with. On the other hand, stereo systems provide more accuracy but require establishing feature correspondence with more computational demand. For both approaches, the extended Kalman filter is used as a real-time recursive estimator. The approaches studied are verified with synthetic and real experiments alike.