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Research in Simultaneous Localization And Mapping (SLAM) is increasinglymoving towards richer world representations involving objects and high levelfeatures that enable a semantic model of the world for robots, potentiallyleading to a more meaningful set of robot-world interactions. Many of theseadvances are grounded in state-of-the-art computer vision techniques primarilydeveloped in the context of image-based benchmark datasets, leaving severalchallenges to be addressed in adapting them for use in robotics. In this paper,we derive a formulation for Simultaneous Localization And Mapping (SLAM) thatuses dual quadrics as 3D landmark representations, and show how 2D boundingboxes (such as those typically obtained from visual object detection systems)can directly constrain the quadric parameters. Our paper demonstrates how tojointly estimate the robot pose and dual quadric parameters in factor graphbased SLAM with a general perspective camera, and covers the use-cases of arobot moving with a monocular camera with and without the availability ofadditional depth information.

Dual Quadrics from Object Detection BoundingBoxes as Landmark Representations in SLAM