Inspection tasks using a camera attached to a robot arm could benefit from work carried out by researchers at Carnegie Mellon University’s Robotics Institute. The scientists have shown that a camera attached to the robot’s hand can create a 3D model of its environment and also locate the hand within that 3D world.
The CMU team found they could improve the accuracy of the map by incorporating the arm itself as a sensor, using the angle of its joints to better determine the pose of the camera.
The researchers presented their findings on 17 May at the IEEE International Conference on Robotics and Automation in Stockholm, Sweden.
Placing a camera or other sensor in the hand of a robot has become feasible as sensors have grown smaller and more power-efficient, said Siddhartha Srinivasa, associate professor of robotics at CMU. That’s important, he explained, because robots ‘usually have heads that consist of a stick with a camera on it’.
But an eye in the hand isn’t much good if the robot can’t see its hand and doesn’t know where its hand is relative to objects in its environment. It’s a problem shared with mobile robots that must operate in an unknown environment. A popular solution for mobile robots is called simultaneous localisation and mapping, or SLAM, in which the robot pieces together input from sensors such as cameras, laser radars and wheel odometry to create a 3D map of the new environment and to determine where the robot is within that 3D world.
‘There are several algorithms available to build these detailed worlds, but they require accurate sensors and a ridiculous amount of computation,’ Srinivasa said.
‘Automatically tracking the joint angles enables the system to produce a high-quality map even if the camera is moving very fast or if some of the sensor data is missing or misleading,’ said Matthew Klingensmith, a PhD student working on the project.
The researchers demonstrated their Articulated Robot Motion for SLAM (ARM-SLAM) using a small depth camera attached to a lightweight manipulator arm, the Kinova Mico. In using it to build a 3D model of a bookshelf, they found that it produced reconstructions equivalent or better to other mapping techniques.
‘We still have much to do to improve this approach, but we believe it has huge potential for robot manipulation,’ Srinivasa said.
Toyota, the US Office of Naval Research and the National Science Foundation supported the research.
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