QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAM
This addresses object-oriented SLAM for robotics and computer vision applications, presenting an incremental improvement by integrating quadric representations into existing SLAM frameworks.
The paper tackles the problem of simultaneous localization and mapping (SLAM) by using 2D object detections to estimate 3D quadric surfaces for objects and camera positions, resulting in a formulation that compactly represents object size, position, and orientation with a novel geometric error.
In this paper, we use 2D object detections from multiple views to simultaneously estimate a 3D quadric surface for each object and localize the camera position. We derive a SLAM formulation that uses dual quadrics as 3D landmark representations, exploiting their ability to compactly represent the size, position and orientation of an object, and show how 2D object detections can directly constrain the quadric parameters via a novel geometric error formulation. We develop a sensor model for object detectors that addresses the challenge of partially visible objects, and demonstrate how to jointly estimate the camera pose and constrained dual quadric parameters in factor graph based SLAM with a general perspective camera.