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.

本文研究了利用多个视角的 2D 目标检测结果，同时估计每个目标的 3D 四面体表面以及相机位置，并提出了利用二次曲面作为 3D 地标描述符的 SLAM 方法，通过新颖的几何误差公式将 2D 目标检测的结果直接约束二次曲面参数；同时，针对目标局部可见性的挑战，开发了一种传感器模型来优化目标检测器的效果，最后使用基于因子图的方法联合估计相机位姿和约束的二次曲面参数。