State Estimation
205 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Libraries
Use these libraries to find State Estimation models and implementationsMost implemented papers
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
MIT-SPARK/Kimera
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We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM).
SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
MegviiDetection/video_analyst
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Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch(G1), classification score without ambiguity(G2), tracking without prior knowledge(G3), and estimation quality score(G4).
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
MIT-SPARK/Kimera
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We propose instead to tightly couple mesh regularization and state estimation by detecting and enforcing structural regularities in a novel factor-graph formulation.
Learning neural state-space models: do we need a state estimator?
forgi86/sysid-neural-estimator
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In recent years, several algorithms for system identification with neural state-space models have been introduced.
ATOM: Accurate Tracking by Overlap Maximization
visionml/pytracking
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CVPR 2019
We argue that this approach is fundamentally limited since target estimation is a complex task, requiring high-level knowledge about the object.
A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors
We highlight that our system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization.
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
We validate the performance of our system on public datasets and through real-world experiments with multiple sensors.
Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
astrohiro/ncm
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This paper presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM).
Model-free Vehicle Tracking and State Estimation in Point Cloud Sequences
The code and protocols for our benchmark and algorithm are available at https://github. com/TuSimple/LiDAR_SOT/.
Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors
tu-rbo/differentiable-particle-filters
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We present differentiable particle filters (DPFs): a differentiable implementation of the particle filter algorithm with learnable motion and measurement models.
