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Simultaneous Localization and Mapping
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Simultaneous localization and mapping (SLAM) is the task of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
<span style="color:grey; opacity: 0.6">( Image credit: ORB-SLAM2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/utiasasrl/crystal_ball_nav
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
https://github.com/utiasasrl/utin3d
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
https://github.com/victorprad/InfiniTAM
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
https://github.com/wuxiaolang/Cube_SLAM_wu
https://github.com/xiaofeng-han-res/mf-rv
Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision
https://github.com/yungecui/link3d
BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.