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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/flysoaryun/lf-vislam
공식 코드https://github.com/funcdecl/df-slam
공식 코드https://github.com/gktejus/DMF-Synch
공식 코드https://github.com/gradslam/gradslam
공식 코드https://github.com/great-whu/dba-fusion
공식 코드https://github.com/hannahhaensen/dynamon
공식 코드DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields
https://github.com/hku-mars/r3live
공식 코드https://github.com/hlzz/DeepMatchVO
공식 코드Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation
https://github.com/horizon-research/pointcloud-pipeline
공식 코드Tigris: Architecture and Algorithms for 3D Perception in Point Clouds
https://github.com/i3tyc/adaptslam
공식 코드AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization
https://github.com/igmr-rwth/randt-slam
공식 코드RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions Transform
https://github.com/iis-esslingen/nerf-3dgs-benchmark
공식 코드https://github.com/iis-esslingen/rover_benchmark
공식 코드https://github.com/iis-esslingen/vi-slam_lc_benchmark
공식 코드https://github.com/ivipsourcecode/dxslam
공식 코드DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
https://github.com/jarvishou829/sups
공식 코드SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving
https://github.com/jarvisyjw/gv-bench
공식 코드https://github.com/jiawei-mo/3d_place_recognition
공식 코드A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors
https://github.com/jingweisong/bdis-slam
공식 코드BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery
https://github.com/jixingwu/ss-lcd
공식 코드Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
https://github.com/junzhang2016/NTU4DRadLM
공식 코드NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
https://github.com/jzubizarreta/dsm
공식 코드https://github.com/kev-in-ta/uncle-slam
공식 코드https://github.com/kun150kun/eslam-survey
공식 코드Event-based Simultaneous Localization and Mapping: A Comprehensive Survey
https://github.com/lab-of-ai-and-robotics/bayesian_nerf
공식 코드Bayesian NeRF: Quantifying Uncertainty with Volume Density for Neural Implicit Fields
https://github.com/lab-of-ai-and-robotics/gs_icp_slam
공식 코드https://github.com/laliwang/lcp-fusion
공식 코드LCP-Fusion: A Neural Implicit SLAM with Enhanced Local Constraints and Computable Prior
https://github.com/larocs/queenscamp-dataset
공식 코드https://github.com/lastbasket/endo-2dtam
공식 코드https://github.com/leomingyangli/semanticslam
공식 코드SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization
https://github.com/lihaoy-ux/dqo-map
공식 코드DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting
https://github.com/lppllppl920/sage-slam
공식 코드https://github.com/machineperceptionlab/qq-slam
공식 코드https://github.com/mameng1/GraphLineMatching
공식 코드Robust Line Segments Matching via Graph Convolution Networks
https://github.com/martinruenz/co-fusion
공식 코드Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.