Point Anywhere: Directed Object Estimation from Omnidirectional Images
This addresses robot navigation constraints for users by eliminating position and arm orientation limitations, though it appears incremental as it builds on existing detection methods.
The paper tackles the problem of estimating object direction from pointing gestures in robot navigation using omnidirectional images, achieving highly accurate estimation by repeatedly extracting regions of interest and projecting them onto perspective images, with training further improving accuracy.
One of the intuitive instruction methods in robot navigation is a pointing gesture. In this study, we propose a method using an omnidirectional camera to eliminate the user/object position constraint and the left/right constraint of the pointing arm. Although the accuracy of skeleton and object detection is low due to the high distortion of equirectangular images, the proposed method enables highly accurate estimation by repeatedly extracting regions of interest from the equirectangular image and projecting them onto perspective images. Furthermore, we found that training the likelihood of the target object in machine learning further improves the estimation accuracy.