Tracking-Based Depth Estimation of Metallic Pieces for Robotic Guidance

摘要

In order to perform safe robotic interventions in harsh environments it is necessary to help the robotic operator with a Human-Robot Interface that provides multimodal interactions, from low level interaction methods to bilateral teleoperation with force feedback. These interaction modalities, though, rely purely on the operator's skills. With the objective of providing a safer system, higher-level applications can be integrated in the interface in order to provide some help to the operator, without relying uniquely on his/her capacities. This paper presents a novel object recognition and tracking system which runs in real-time on the robot while the operator is operating it. The tracking system enters in the teleoperation loop and helps the operator to achieve the requested goals. The system is optimized to track featureless objects such as metallic plates, metallic connectors and monochromatic objects. Moreover, the algorithm provides improvements with respect to previous tracking experiments, including depth estimation in order to better interact with the velocity control of the robotic arm when approaching the target, as well as high reliability with partial occlusions. This vision-based control system is used in real interventions in hazardous environments, in order to track and manipulate metallic parts of scientific and engineering machines, giving a performance success over 95%, and reaching the 100% under the remote human supervision.