Simultaneous Localization and Mapping
163 papers with code • 0 benchmarks • 21 datasets
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.
( Image credit: ORB-SLAM2 )
Benchmarks
These leaderboards are used to track progress in Simultaneous Localization and Mapping
Libraries
Use these libraries to find Simultaneous Localization and Mapping models and implementationsDatasets
TUM RGB-D
Virtual KITTI
Virtual KITTI 2
InteriorNet
New College
TUM monoVO
Bonn RGB-D Dynamic
SLAM2REF
Most implemented papers
Visual-Inertial Monocular SLAM with Map Reuse
In recent years there have been excellent results in Visual-Inertial Odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness.
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).
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities.
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
fangchangma/sparse-to-dense
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We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image.
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA).
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.
Semi-Dense 3D Reconstruction with a Stereo Event Camera
Event cameras are bio-inspired sensors that offer several advantages, such as low latency, high-speed and high dynamic range, to tackle challenging scenarios in computer vision.
LiDARTag: A Real-Time Fiducial Tag System for Point Clouds
Because of the LiDAR sensors' nature, rapidly changing ambient lighting will not affect the detection of a LiDARTag; hence, the proposed fiducial marker can operate in a completely dark environment.
EndoMapper dataset of complete calibrated endoscopy procedures
Computer-assisted systems are becoming broadly used in medicine.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
MIT-SPARK/Kimera
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Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
