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 )
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Latest papers
DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping
Visual simultaneous localization and mapping (VSLAM) has broad applications, with state-of-the-art methods leveraging deep neural networks for better robustness and applicability.
Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments
Perceptual aliasing and weak textures pose significant challenges to the task of place recognition, hindering the performance of Simultaneous Localization and Mapping (SLAM) systems.
RGBD GS-ICP SLAM
Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications.
VOOM: Robust Visual Object Odometry and Mapping using Hierarchical Landmarks
Meanwhile, local bundle adjustment is performed utilizing the objects and points-based covisibility graphs in our visual object mapping process.
Customizable Perturbation Synthesis for Robust SLAM Benchmarking
To this end, we propose a novel, customizable pipeline for noisy data synthesis, aimed at assessing the resilience of multi-modal SLAM models against various perturbations.
SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization
This approach enables the creation of a semantic map of the environment and ensures reliable camera localization.
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery
Conclusion: The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS.
SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications.
Continuous Pose for Monocular Cameras in Neural Implicit Representation
In this paper, we showcase the effectiveness of optimizing monocular camera poses as a continuous function of time.
Monocular visual simultaneous localization and mapping:(r) evolution from geometry to deep learning-based pipelines
With the rise of deep learning, there is a fundamental change in visual simultaneous localization and mapping (SLAM) algorithms toward developing different modules trained as end-to-end pipelines.