arXiv:1805.07696
RGB-Depth SLAM Review
원문 초록
Simultaneous Localization and Mapping (SLAM) have made the real-time dense reconstruction possible increasing the prospects of navigation, tracking, and augmented reality problems. Some breakthroughs have been achieved in this regard during past few decades and more remarkable works are still going on. This paper presents an overview of SLAM approaches that have been developed till now. Kinect Fusion algorithm, its variants, and further developed approaches are discussed in detailed. The algorithms and approaches are compared for their effectiveness in tracking and mapping based on Root Mean Square error over online available datasets.
함께 살펴볼 논문
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SuperPoint-SLAM3: Augmenting ORB-SLAM3 with Deep Features, Adaptive NMS, and Learning-Based Loop Closure
LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
SupeRANSAC: One RANSAC to Rule Them All
Place Recognition: A Comprehensive Review, Current Challenges and Future Directions
SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM
Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAM
토론
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