Place Recognition using Straight Lines for Vision-based SLAM
Most visual simultaneous localization and mapping systems use point features as their landmarks and adopt point-based feature descriptors to recognize them.
Compared to point landmarks, however, lines have strength in conveying the structural information of the environment.
Despite the benefit, they have not been widely used because lines are more difficult in detecting, tracking, and recognizing, and this delayed the use of lines as landmarks.
In this paper, we propose a place recognition algorithm using straight line features, which enables reliable detection of loop closures in a large complex environment under significant illumination changes.
A vocabulary tree trained with mean standard-deviation line descriptor is used in finding the candidate matches between keyframes, and a Bayesian filtering framework enables reliable keyframe matching for large-scale loop closures.
The proposed algorithm is compared with state-of-the-art point-based methods using scale-invariant feature transform or speeded up robust features.
The experimental results show that the proposed method outperforms the others in challenging indoor environments
|과제명||사용자 행동양식의 모방 학습 기반 휴먼 아바타의 창발적 복합행동 생성 기술|