DB기반의 특징 매칭과 RANSAC기반의 다중 평면 방식을 이용한 AR기반 장애물 감지 시스템
DB-Based Feature Matching and RANSAC-Based Multiplane Method for Obstacle Detection System in AR
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초록
본 논문에서는 날씨와 같은 외부 환경요인에도 강건하게 동작할 수 있는 장애물 감지 기법을 제안한다. 특히, DB 기반의 특징 매칭과 RANSAC(RANdom SAample Consensus)기반의 다중 평면 방식을 통해증강현실(Augmented Reality, AR)에서 정확하게 위험 상황을 알려줄 수 있는 장애물 감지 시스템을제안한다. RGB카메라로부터 얻은 영상을 기반으로 장애물을 검출하는 접근법은 영상에 의존하기 때문에 조명에 따른 특징 검출이 부정확하고, 조명이나 자연광 또는 날씨의 영향을 받기 때문에 장애물검출이 어려워진다. 또한, 복잡한 지형에서 생성되는 다수의 평면은 장애물을 감지하는데 있어서 오차가커지는 원인이 된다. 이 문제를 완화하기 위해 본 논문에서는 DB기반의 특징 매칭을 통해 조명에 관계없이 장애물을 효율적이고 정확하게 감지한다. 또한, 다중 평면을 RANSAC을 통해 단일 평면으로 정규화하여 특징점을 분류하기 위한 기준을 새롭게 계산한다. 결과적으로 제안하는 방법은 조명, 자연광, 날씨에 관계없이 효율적으로 장애물을 감지할 수 있고, 높낮이나 다른 지형에서도 안정적으로 표면을감지할 수 있기 때문에 사용자의 안전성 확보에 활용할 수 있을 거라 기대한다. 제안하는 방법은 모바일디바이스에서 실험한 결과가 대부분 안정적으로 실내/외의 장애물들을 인지하였다.
Abstract
In this paper, we propose an obstacle detection method that can operate robustly even in external environmental factors such as weather. In particular, we propose an obstacle detection system that can accurately inform dangerous situations in AR through DB-based feature matching and RANSAC-based multiplane method. Since the approach to detecting obstacles based on images obtained by RGB cameras relies on images, the feature detection according to lighting is inaccurate, and it becomes difficult to detect obstacles because they are affected by lighting, natural light, or weather. In addition, it causes a large error in detecting obstacles on a number of planes generated due to complex terrain. To alleviate this problem, this paper efficiently and accurately detects obstacles regardless of lighting through DB-based feature matching. In addition, a criterion for classifying feature points is newly calculated by normalizing multiple planes to a single plane through RANSAC. As a result, the proposed method can efficiently detect obstacles regardless of lighting, natural light, and weather, and it is expected that it can be used to secure user safety because it can reliably detect surfaces in high and low or other terrains. In the proposed method, most of the experimental results on mobile devices reliably recognized indoor/outdoor obstacles.
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