Mobile Robot Digital Landmarks Recognition and Finger Writing Maneuver. Este artículo no está disponible.Mobile Robot Digital Landmarks Recognition and Finger Writing Maneuver

This book presents an image-based mobile robot guidance system in an indoor space with installed artificial ceiling landmarks and the robot is maneuver by a vision-based fingertip-writing character recognition system. The overall system, including an omni-directional mobile robot motion control, landmark image processing and image recognition, is implemented on a single FPGA chip with one CMOS image sensor. The proposed feature representation of the artificial ceiling land-marks is invariant with respect to rotation and translation. To enhance recognition accuracy, landmark classification is performed after the mobile robot is moved to a position such that the ceiling landmark is located in the upright-top corner position of the robot’s camera image. The accuracy of the proposed artificial ceiling landmark recognition system using nearest neighbor classification is 100% in our experiments and the proposed fingertip-writing recognition system provides an easy-to-use and accurate visual character input method.

This book presents an image-based mobile robot guidance system in an indoor space with installed artificial ceiling landmarks and the robot is maneuver by a vision-based fingertip-writing character recognition system. The overall system, including an omni-directional mobile robot motion control, landmark image processing and image recognition, is implemented on a single FPGA chip with one CMOS image sensor. The proposed feature representation of the artificial ceiling land-marks is invariant with respect to rotation and translation. To enhance recognition accuracy, landmark classification is performed after the mobile robot is moved to a position such that the ceiling landmark is located in the upright-top corner position of the robot’s camera image. The accuracy of the proposed artificial ceiling landmark recognition system using nearest neighbor classification is 100% in our experiments and the proposed fingertip-writing recognition system provides an easy-to-use and accurate visual character input method.