ObjectMatch: Robust Registration using Canonical Object Correspondences

TL;DR

ObjectMatch introduces a semantic, object-centric approach to camera pose estimation that leverages canonical object correspondences, significantly improving registration accuracy especially in low-overlap scenarios.

Contribution

The paper proposes a neural network for predicting per-pixel canonical object correspondences and integrates it into a pose estimation framework, enhancing RGB-D SLAM robustness.

Findings

Registration recall increased from 24% to 45% in low-overlap pairs.

Outperforms state-of-the-art SLAM methods in challenging scenarios.

Achieves over 35% reduction in trajectory error across multiple scenes.

Abstract

We present ObjectMatch, a semantic and object-centric camera pose estimator for RGB-D SLAM pipelines. Modern camera pose estimators rely on direct correspondences of overlapping regions between frames; however, they cannot align camera frames with little or no overlap. In this work, we propose to leverage indirect correspondences obtained via semantic object identification. For instance, when an object is seen from the front in one frame and from the back in another frame, we can provide additional pose constraints through canonical object correspondences. We first propose a neural network to predict such correspondences on a per-pixel level, which we then combine in our energy formulation with state-of-the-art keypoint matching solved with a joint Gauss-Newton optimization. In a pairwise setting, our method improves registration recall of state-of-the-art feature matching, including from 24% to 45% in pairs with 10% or less inter-frame overlap. In registering RGB-D sequences, our method outperforms cutting-edge SLAM baselines in challenging, low-frame-rate scenarios, achieving more than 35% reduction in trajectory error in multiple scenes.