Scale Estimation with Dual Quadrics for Monocular Object SLAM

Abstract

The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we formulate an optimization problem to make the scaled dimensions of objects conform to the distribution of their sizes in the physical world, without relying on any prior information about gravity direction. The dual quadric is adopted to represent objects for its ability to describe objects compactly and accurately, thus providing reliable dimensions for scale estimation. In the proposed monocular object-level SLAM system, semantic objects are initialized first from fitted 3-D oriented bounding boxes and then further optimized under constraints of 2-D detections and 3-D map points. Experiments on indoor and outdoor public datasets show that our approach outperforms existing methods in terms of accuracy and robustness.