Simultaneous Localization and Mapping
131 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
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
Most implemented papers
Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment
The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL).
Optimal Target Shape for LiDAR Pose Estimation
However, symmetric shapes lead to pose ambiguity when using sparse sensor data such as LiDAR point clouds and suffer from the quantization uncertainty of the LiDAR.
LF-VISLAM: A SLAM Framework for Large Field-of-View Cameras with Negative Imaging Plane on Mobile Agents
As loop closure on wide-FoV panoramic data further comes with a large number of outliers, traditional outlier rejection methods are not directly applicable.
ORB-SLAM: a Versatile and Accurate Monocular SLAM System
This paper presents ORB-SLAM, a feature-based monocular SLAM system that operates in real time, in small and large, indoor and outdoor environments.
MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System
The basis for most vision based applications like robotics, self-driving cars and potentially augmented and virtual reality is a robust, continuous estimation of the position and orientation of a camera system w. r. t the observed environment (scene).
Monocular LSD-SLAM Integration within AR System
In this paper, we cover the process of integrating Large-Scale Direct Simultaneous Localization and Mapping (LSD-SLAM) algorithm into our existing AR stereo engine, developed for our modified "Augmented Reality Oculus Rift".
SLAM with Objects using a Nonparametric Pose Graph
The \textit{data association} and \textit{simultaneous localization and mapping} (SLAM) problems are, individually, well-studied in the literature.
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.
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.
CNN-SVO: Improving the Mapping in Semi-Direct Visual Odometry Using Single-Image Depth Prediction
Reliable feature correspondence between frames is a critical step in visual odometry (VO) and visual simultaneous localization and mapping (V-SLAM) algorithms.