Hierarchical Object Map Estimation for Efficient and Robust Navigation

Abstract

We propose a hierarchical representation of objects, where the representation of each object is allowed to change based on the quality of accumulated measurements. We initially estimate each object as a 2D bounding box or a 3D point, encoding only the geometric properties that can be well-constrained using limited viewpoints. With additional measurements, we allow each object to become a higher dimensional 3D volumetric model for improved reconstruction accuracy and collision-testing. Our Hierarchical Object Map Estimation (HOME) is robust to deficiencies in viewpoints and allows planning safe and efficient trajectories around object obstacles using a monocular camera. We demonstrate the advantages of our approach on a real-world TUM dataset and during visual-inertial navigation of a quad-rotor in simulation.