首页|New Findings from Carnegie Mellon University in the Area of Robotics Described ( Unifying Representation and Calibration With 3d Foundation Models)
New Findings from Carnegie Mellon University in the Area of Robotics Described ( Unifying Representation and Calibration With 3d Foundation Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ro botics. According to news reporting out of Pittsburgh, Pennsylvania, by NewsRx e ditors, research stated, “Representing the environment is a central challenge in robotics, and is essential for effective decision-making. Traditionally, before capturing images with a manipulator-mounted camera, users need to calibrate the camera using a specific external marker, such as a checkerboard or AprilTag.” Our news journalists obtained a quote from the research from Carnegie Mellon Uni versity, “However, recent advances in computer vision have led to the developmen t of 3D foundation models. These are large, pre-trained neural networks that can establish fast and accurate multi-view correspondences with very few images, ev en in the absence of rich visual features. This paper advocates for the integrat ion of 3D foundation models into scene representation approaches for robotic sys tems equipped with manipulator-mounted RGB cameras. Specifically, we propose the Joint Calibration and Representation (JCR) method. JCR uses RGB images, capture d by a manipulator-mounted camera, to simultaneously construct an environmental representation and calibrate the camera relative to the robot’s end-effector, in the absence of specific calibration markers. The resulting 3D environment repre sentation is aligned with the robot’s coordinate frame and maintains physically accurate scales.”
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaEmerging TechnologiesMachine LearningRobotRobo ticsCarnegie Mellon University
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