https://github.com/1989Ryan/Semantic_SLAM
공식 코드Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Papers with Code 아카이브
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/AlexS28/SABER
공식 코드SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots
https://github.com/AnshanTJU/FILD
공식 코드Fast and Incremental Loop Closure Detection Using Proximity Graphs
https://github.com/BeipengMu/objectSLAM
공식 코드https://github.com/CapsuleEndoscope/EndoSLAM
공식 코드https://github.com/CnnDepth/MAOMaps
공식 코드MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality Assessment
https://github.com/CnnDepth/tx2_fcnn_node
공식 코드Real-time Vision-based Depth Reconstruction with NVidia Jetson
https://github.com/DapengFeng/cartgs
공식 코드CaRtGS: Computational Alignment for Real-Time Gaussian Splatting SLAM
https://github.com/GradientSpaces/LoopSplat
공식 코드https://github.com/GuidoManni/BodySLAM
공식 코드BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications
https://github.com/IRMVLab/SemGauss-SLAM
공식 코드https://github.com/IRVLab/so_dso_place_recognition
공식 코드A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors
https://github.com/JiawangBian/sc_depth_pl
공식 코드https://github.com/JingwenWang95/DSP-SLAM
공식 코드https://github.com/KumarRobotics/kr_autonomous_flight
공식 코드Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
https://github.com/KumarRobotics/sloam
공식 코드Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
https://github.com/LCAD-UFES/WNN-CNN-GL
공식 코드https://github.com/LeeBY68/Hier-SLAM
공식 코드Hier-SLAM: Scaling-up Semantics in SLAM with a Hierarchically Categorical Gaussian Splatting
https://github.com/Li-Jinquan/Salient-Euroc
공식 코드Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze
https://github.com/LiliMeng/btrf
공식 코드Backtracking Regression Forests for Accurate Camera Relocalization
https://github.com/MARSLab-UMN/vi_depth_completion
공식 코드https://github.com/MILAB-IIT-CV/ATDN_vSLAM
공식 코드https://github.com/MIT-SPARK/Kimera
공식 코드Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/MaXvanHeLL/ARift
공식 코드https://github.com/MaXvanHeLL/LSD-SLAM
공식 코드https://github.com/MaaniGhaffari/cvo-rgbd
공식 코드https://github.com/MetaSLAM/GPRS
공식 코드General Place Recognition Survey: Towards the Real-world Autonomy Age
https://github.com/MurpheyLab/CPL-SLAM
공식 코드https://github.com/SenseRoboticsLab/AQUA-SLAM
공식 코드AQUA-SLAM: Tightly-Coupled Underwater Acoustic-Visual-Inertial SLAM with Sensor Calibration
https://github.com/SnowCarter/Intensity_based_LiDAR_SLAM
공식 코드Real-Time Simultaneous Localization and Mapping with LiDAR intensity
https://github.com/TUMFTM/orbslam-map-saving-extension
공식 코드Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension
https://github.com/TaoWangzj/CorrAdaptor
공식 코드CorrAdaptor: Adaptive Local Context Learning for Correspondence Pruning
https://github.com/UMich-BipedLab/LiDARTag
공식 코드https://github.com/UMich-BipedLab/global_pose_estimation_for_optimal_shape
공식 코드https://github.com/UsmanMaqbool/why-so-deep
공식 코드Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
https://github.com/VRI-UFPR/BeyondSight
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.