Simultaneous Localization and Mapping
134 papers with code • 0 benchmarks • 18 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
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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
S3E
Latest papers with no code
MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements
We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM.
GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DSPO layer for bundle adjustment which optimizes the pose and depth of keyframes along with the scale of the monocular depth.
EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting
Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsule robots.
DVN-SLAM: Dynamic Visual Neural SLAM Based on Local-Global Encoding
Recent research on Simultaneous Localization and Mapping (SLAM) based on implicit representation has shown promising results in indoor environments.
A Belief Propagation Algorithm for Multipath-based SLAM with Multiple Map Features: A mmWave MIMO Application
We develop a Bayesian model for sequential detection and estimation of interacting MF model parameters, MF states and mobile agent's state including position and orientation.
Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning
In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors.
Loopy-SLAM: Dense Neural SLAM with Loop Closures
Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resulting in distorted maps.
Particle Filter SLAM for Vehicle Localization
Simultaneous Localization and Mapping (SLAM) presents a formidable challenge in robotics, involving the dynamic construction of a map while concurrently determining the precise location of the robotic agent within an unfamiliar environment.
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction
Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints.
SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM
We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting.
