SOTAVerified

Simultaneous Localization and Mapping

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 )

Papers

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Showing 341–350 of 572 papers

TitleStatusHype
BEyond observation: an approach for ObjectNavCode0
Towards bio-inspired unsupervised representation learning for indoor aerial navigation—0
MAOMaps: A Photo-Realistic Benchmark For vSLAM and Map Merging Quality AssessmentCode1
Embedded Vision for Self-Driving on Forest Roads—0
Unsupervised Scale-consistent Depth Learning from VideoCode1
Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation—0
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping—0
Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory OptimizationCode1
SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition—0
Improved Real-Time Monocular SLAM Using Semantic Segmentation on Selective Frames—0
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