저사양 환경에서 ROI-ORB 경량 특징점 추출 및 정합 기법 = ROI-ORB: Lightweight Feature Extraction and Matching for Resource-Constrained Environments

다국어 초록 (Multilingual Abstract)

Visual SLAM is essential for resource-constrained embedded systems, yet global
feature-based approaches often struggle with high computational loads and instability
in repetitive environments. Conventional methods process entire images without
structural discrimination, causing resource waste and frequent false matches in
texture-poor regions, which hinders pose estimation stability.

To address these challenges, this paper proposes ROI-ORB, a method that defines
Regions of Interest (ROI) based on line-segment detection and selectively extracts
features within these areas. The algorithm restricts ORB operations using line-based
masks to reduce redundant computation and reinforces matching accuracy through a
filtering process that integrates disparity cues with epipolar geometric constraints.

Experiments on the KITTI Stereo 2015 dataset demonstrate that ROI-ORB achieves 107.73
FPS, a 5.5× speed improvement over global ORB, proving its efficiency. It also
records an Inlier Ratio of 91.68%, confirming strong structural reliability. In
conclusion, ROI-ORB is validated as an effective front-end solution balancing
real-time performance and matching quality in resource-constrained environments.

목차 (Table of Contents)

• I 서 론 1
• II 관련 연구 6
• 2.1 ORB: 전역 특징점 기반 접근 6
• 2.2 LSD: 구조적 기반 접근 8
• 2.3 SuperPoint: 딥러닝 기반 접근 11
• 2.4 임베디드 환경 기반 접근 14
• 2.5 기존 연구의 한계 15
• III 제안 기법 18
• 3.1 제안 기법 개요 18
• 3.2 선 기반 ROI 생성 21
• 3.3 ROI-ORB 특징점 추출 23
• 3.4 ROI-ORB 정합 과정 25
• IV 실험 및 결과 28
• 4.1 ROI-ORB 실험 환경 28
• 4.2 최소 선분 길이 기반 ROI 안정성 평가 30
• 4.3 ORB 대비 ROI-ORB 성능 평가 35
• 4.4 세 기법 대비 ROI-ORB 성능 평가 38
• V 결 론 43
• 참 고 문 헌 45
• 영 문 초 록 49