Keyframe-based monocular SLAM: design, survey, and future directions
원문 초록
Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Survey on Prompt Tuning
A Survey on Latent Reasoning
A Survey on Vision-Language-Action Models for Autonomous Driving
From Tiny Machine Learning to Tiny Deep Learning: A Survey
A Comprehensive Survey on Continual Learning in Generative Models
A Survey on World Models Grounded in Acoustic Physical Information
토론
이 논문과 연결된 포럼 글이 아직 없습니다.