Papers with Code 아카이브

State Estimation

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hkust-aerial-robotics/vins-fisheye

A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors

https://github.com/ir413/mvp

Masked Visual Pre-training for Motor Control

https://github.com/joaofcmota/DistributedOptimizationWithLocalDomains

Distributed Robust Power System State Estimation

https://github.com/kalmannet/rtsnet_tsp

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

https://github.com/kyegomez/PALM-E

PaLM-E: An Embodied Multimodal Language Model

https://github.com/liangp/mcstn-3dsot

Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences

https://github.com/ma8sa/Undersrtanding-Dynamic-Scenes-using-MR-GCN

Understanding Dynamic Scenes using Graph Convolution Networks

https://github.com/makarandtapaswi/Real2Sim_CoRL2020

Learning Object Manipulation Skills via Approximate State Estimation from Real Videos

https://github.com/martin-danelljan/Continuous-ConvOp

ATOM: Accurate Tracking by Overlap Maximization

https://github.com/martin-danelljan/ECO

ATOM: Accurate Tracking by Overlap Maximization

https://github.com/mcdermatt/icet

Enhanced Laser-Scan Matching with Online Error Estimation for Highway and Tunnel Driving

https://github.com/microsoft/AirSim-Drone-Racing-Lab

AirSim Drone Racing Lab

https://github.com/mingyuw/tracker_209as

SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines

https://github.com/nbhusal/Power-System-State-Estimation

Deep Ensemble Learning-based Approach to Real-time Power System State Estimation

https://github.com/nicopao/CGM_prediction_data

MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas

https://github.com/nsinghal7/Kimera-VIO-NetVLAD

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

https://github.com/nsinghal7/Kimera-VIO-ROS-NetVLAD

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

https://github.com/nyu-wireless/mmwRobotNav

Learning to Explore using Active Neural SLAM

https://github.com/ognjenkundacina/graph-neural-network-state-estimation

State Estimation in Electric Power Systems Leveraging Graph Neural Networks

https://github.com/pjrambo/VINS-Fusion-gpu

A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors

https://github.com/rafcy/HarpyTM

Extracting the fundamental diagram from aerial footage

https://github.com/rddy/ASE

Assisted Perception: Optimizing Observations to Communicate State

https://github.com/rkcosner/mrcbf_segway_sim_IROS_2021

Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State

https://github.com/rwth-irt/gnssfgo

Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network

https://github.com/rwth-irt/onlinefgo

Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network

https://github.com/sabeaussan/ROS_Unity

Solving Rubik's Cube with a Robot Hand

https://github.com/scope-lab/pyest

Split Happens! Imprecise and Negative Information in Gaussian Mixture Random Finite Set Filtering

https://github.com/tiboat/BackpropKF_Reproduction

Backprop KF: Learning Discriminative Deterministic State Estimators

https://github.com/umngangaliqiu/dnn4gnetworking

Real-time Power System State Estimation and Forecasting via Deep Neural Networks

https://github.com/wzjoriv/Hion

Receding Hamiltonian-Informed Optimal Neural Control and State Estimation for Closed-Loop Dynamical Systems

https://github.com/xuefeng-zhu5/cdaat

ATOM: Accurate Tracking by Overlap Maximization

https://github.com/xuhao1/VINS-Fisheye

A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors

https://github.com/ylabbe/permutandis

Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.