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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 471–480 of 572 papers

TitleStatusHype
Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM—0
Not Only Look But Observe: Variational Observation Model of Scene-Level 3D Multi-Object Understanding for Probabilistic SLAMCode0
Real-time Vision-based Depth Reconstruction with NVidia JetsonCode1
Segway DRIVE Benchmark: Place Recognition and SLAM Data Collected by A Fleet of Delivery Robots—0
Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environmentCode0
SLAM Endoscopy enhanced by adversarial depth prediction—0
PyRobot: An Open-source Robotics Framework for Research and BenchmarkingCode1
Movable-Object-Aware Visual SLAM via Weakly Supervised Semantic Segmentation—0
eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA Platform—0
Y-GAN: A Generative Adversarial Network for Depthmap Estimation from Multi-camera Stereo Images—0
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