https://github.com/JonasFrey96/Kimera-Semantics
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
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/LakshmiTeja17/NNFL-Project
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/LeonSun0101/CD-SD
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/M2219/pyOrbSLAM
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
https://github.com/MIT-SPARK/Kimera-Evaluation
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/MIT-SPARK/Kimera-RPGO
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-Semantics
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/MIT-SPARK/Kimera-VIO
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/MIT-SPARK/Kimera-VIO-Evaluation
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/MIT-SPARK/Kimera-VIO-ROS
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/RKinDLab/ros2_psd_pcb_reloc
https://github.com/Shiaoming/vid2depth
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
https://github.com/TianXiaoRui/Monocular_DXSLAM
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
https://github.com/ToniRV/Kimera-VIO-Evaluation
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
https://github.com/UMich-BipedLab/automatic_lidar_camera_calibration
https://github.com/UMich-BipedLab/extrinsic_lidar_camera_calibration
https://github.com/UMich-BipedLab/sync_lidartag_apriltag
https://github.com/VladimirYugay/Gaussian-SLAM
Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting
https://github.com/VladimirYugay/MAGiC-SLAM
https://github.com/WhutChengjun/VI-ORB
https://github.com/ZuoJiaxing/Learn-ORB-VIO-Stereo-Mono
https://github.com/a-taniguchi/SpCoSLAM
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
https://github.com/a-taniguchi/SpCoSLAM2
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
https://github.com/a-taniguchi/SpCoSLAM_evaluation
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
https://github.com/allenai/robustnav
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
https://github.com/aseslamian/Det-SLAM
Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2
https://github.com/baklawe/CoBe
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
https://github.com/brytsknguyen/M-LOAM
Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration
https://github.com/camma-public/ssg-qa
CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80
https://github.com/camma-public/ssg-vqa
CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80
https://github.com/cedrusx/dxslam_ros
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
https://github.com/csiro-robotics/uncertainty-lpr
STUN: Self-Teaching Uncertainty Estimation for Place Recognition
https://github.com/dtc111111/multi-modal-uav
SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
https://github.com/ebnerluca/uw_depth
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets
https://github.com/enteguo/LearnVIORBnorosgai2
https://github.com/fangchangma/sparse-to-dense.pytorch
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.