SOS-Match: 非構造環境におけるオープンセットロバスト対応探索とロボット自己位置推定のためのセグメンテーション
SOS-Match: Segmentation for Open-Set Robust Correspondence Search and Robot Localization in Unstructured Environments
ゼロショットセグメンテーションで物体マスクを抽出し、その幾何学的関係を利用して非構造環境での自己位置推定を高精度・高速に行うフレームワークを提案。
著者: Annika Thomas, Jouko Kinnari, Parker Lusk, Kota Kondo, Jonathan P. How
分類: cs.RO, cs.CV
原文アブストラクト
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/.
