https://github.com/XunshanMan/SoSLAM
공식 코드SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints
Papers with Code 아카이브
Simultaneous Localization and Mapping
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 0
- 보존된 평가 기록
- 0
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/YuePanEdward/MULLS
공식 코드MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square
https://github.com/adrianbojko/consinv-dataset
공식 코드Self-Improving SLAM in Dynamic Environments: Learning When to Mask
https://github.com/ai4ce/NYC-Event-VPR
공식 코드https://github.com/aimi-lab/robust-pose-estimator
공식 코드Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/airlab-polimi/c-slam
공식 코드Object Structural Points Representation for Graph-based Semantic Monocular Localization and Mapping
https://github.com/ais-bonn/lidar_mars_registration
공식 코드https://github.com/alejandrofontan/vslam-lab
공식 코드VSLAM-LAB: A Comprehensive Framework for Visual SLAM Methods and Datasets
https://github.com/alterpang/multi_orb_slam
공식 코드Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation
https://github.com/anshan-ar/fild
공식 코드Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs
https://github.com/arplaboratory/coped
공식 코드https://github.com/autonomousvision/kitti360labeltool
공식 코드KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D
https://github.com/autonomousvision/kitti360scripts
공식 코드KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D
https://github.com/bambvalley/p2u-slam
공식 코드P2U-SLAM: A Monocular Wide-FoV SLAM System Based on Point Uncertainty and Pose Uncertainty
https://github.com/banafshebamdad/InCrowd-VI
공식 코드https://github.com/bmhopkinson/hyslam
공식 코드High-resolution Ecosystem Mapping in Repetitive Environments Using Dual Camera SLAM
https://github.com/bogus2000/NOLBO
공식 코드https://github.com/changhao-chen/deep-learning-localization-mapping
공식 코드https://github.com/csiro-robotics/LoGG3D-Net
공식 코드LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition
https://github.com/cv4ra/sota-place-recognitioner
공식 코드Place Recognition: A Comprehensive Review, Current Challenges and Future Directions
https://github.com/cvg/nice-slam
공식 코드https://github.com/danini/superansac
공식 코드https://github.com/digiamm/ba_md_slam
공식 코드https://github.com/dlr-rm/umf
공식 코드https://github.com/dongjing3309/minisam
공식 코드miniSAM: A Flexible Factor Graph Non-linear Least Squares Optimization Framework
https://github.com/doongli/awesome-implicit-nerf-slam
공식 코드How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
https://github.com/endomapper/EM_Dataset-GeometricCalibration
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/endomapper/EM_Dataset-ToolSegmentation
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/endomapper/em_dataset-orbslam3validation
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/endomapper/em_dataset-photometriccalibration
공식 코드EndoMapper dataset of complete calibrated endoscopy procedures
https://github.com/eric-yyjau/pytorch-deepFEPE
공식 코드Deep Keypoint-Based Camera Pose Estimation with Geometric Constraints
https://github.com/eriksandstroem/point-slam
공식 코드https://github.com/eriksandstroem/splat-slam
공식 코드Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians
https://github.com/facebookresearch/pyrobot
공식 코드PyRobot: An Open-source Robotics Framework for Research and Benchmarking
https://github.com/fangchangma/sparse-to-dense
공식 코드Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/flysoaryun/lf-slam
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.