SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments

TL;DR

SOS-Match is a new framework that improves object detection, matching, and robot localization in unstructured environments by leveraging zero-shot segmentation and geometric consistency, outperforming classical and learning-based methods in robustness and efficiency.

Contribution

The paper introduces SOS-Match, a novel segmentation-based approach that enhances robustness and computational efficiency in open-set object matching and robot localization.

Findings

More robust to lighting and appearance changes than classical methods.

Provides greater viewpoint invariance than learning-based features.

Localizes up to 46x faster with significantly smaller maps.

Abstract

We present SOS-Match, a novel framework for detecting and matching objects in unstructured environments. Our system consists of 1) a front-end mapping pipeline using a zero-shot segmentation model to extract object masks from images and track them across frames and 2) a frame alignment pipeline that uses the geometric consistency of object relationships to efficiently localize across a variety of conditions. We evaluate SOS-Match on the Batvik seasonal dataset which includes drone flights collected over a coastal plot of southern Finland during different seasons and lighting conditions. Results show that our approach is more robust to changes in lighting and appearance than classical image feature-based approaches or global descriptor methods, and it provides more viewpoint invariance than learning-based feature detection and description approaches. SOS-Match localizes within a reference map up to 46x faster than other feature-based approaches and has a map size less than 0.5% the size of the most compact other maps. SOS-Match is a promising new approach for landmark detection and correspondence search in unstructured environments that is robust to changes in lighting and appearance and is more computationally efficient than other approaches, suggesting that the geometric arrangement of segments is a valuable localization cue in unstructured environments. We release our datasets at https://acl.mit.edu/SOS-Match/.