https://github.com/mchancan/deepseqslam
공식 코드DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
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/metra4ok/hpointloc
공식 코드HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images
https://github.com/mi-biolab/region-learner
공식 코드Region Prediction for Efficient Robot Localization on Large Maps
https://github.com/michealcarac/VSLAM-Mapping
공식 코드Autonomous Navigation System from Simultaneous Localization and Mapping
https://github.com/mistlab/swarm-slam
공식 코드https://github.com/mistlab/vpr-calibration-and-uncertainty
공식 코드Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition
https://github.com/nail-hnu/esvo2
공식 코드ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras
https://github.com/navganti/SIVO
공식 코드Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
https://github.com/olayasturias/monocular_visual_slam_survey
공식 코드https://github.com/ov2slam/ov2slam
공식 코드OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications
https://github.com/pengyu-team/s3e
공식 코드S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM
https://github.com/peterstratton/volume-droid
공식 코드Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM
https://github.com/qimaqi/continuous-pose-in-nerf
공식 코드Continuous Pose for Monocular Cameras in Neural Implicit Representation
https://github.com/qingshufan/GeneA-SLAM2
공식 코드https://github.com/ramdrop/stun
공식 코드STUN: Self-Teaching Uncertainty Estimation for Place Recognition
https://github.com/risqiutama/ti-slam
공식 코드Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
https://github.com/rmsalinas/LDSO
공식 코드UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers
https://github.com/robot-learning-freiburg/LCDNet
공식 코드LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
https://github.com/rovislab/loopdb
공식 코드LoopDB: A Loop Closure Dataset for Large Scale Simultaneous Localization and Mapping
https://github.com/sair-lab/islam
공식 코드https://github.com/samuel-cerezo/SLAM-Render
공식 코드SLAM&Render: A Benchmark for the Intersection Between Neural Rendering, Gaussian Splatting and SLAM
https://github.com/shahram95/superpointslam3
공식 코드https://github.com/shichaoy/cube_slam
공식 코드https://github.com/shidahe/semidense-lines
공식 코드https://github.com/shuhongll/sgs-slam
공식 코드https://github.com/sjtu-visys/textslam
공식 코드https://github.com/smartroboticslab/mid-fusion
공식 코드MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM
https://github.com/smbonilla/gaussianpancakes
공식 코드https://github.com/snt-arg/ecg2o
공식 코드ecg2o: A Seamless Extension of g2o for Equality-Constrained Factor Graph Optimization
https://github.com/spla-tam/splatam
공식 코드SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
https://github.com/star-center/ros_mp4
공식 코드High-Quality, ROS Compatible Video Encoding and Decoding for High-Definition Datasets
https://github.com/sunghoon031/LCSD_SLAM
공식 코드https://github.com/sunjiayuanro/scfeat
공식 코드Shared Coupling-bridge for Weakly Supervised Local Feature Learning
https://github.com/tberriel/ovo
공식 코드https://github.com/tensorflow/models/tree/master/research/vid2depth
공식 코드Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
https://github.com/tfy14esa/point-slam
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.