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 111–120 of 572 papers

TitleStatusHype
GV-Bench: Benchmarking Local Feature Matching for Geometric Verification of Long-term Loop Closure DetectionCode2
FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration—0
A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM—0
Hyperion - A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAMCode2
Humans as Checkerboards: Calibrating Camera Motion Scale for World-Coordinate Human Mesh Recovery—0
From Perfect to Noisy World Simulation: Customizable Embodied Multi-modal Perturbations for SLAM Robustness BenchmarkingCode2
Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and NavigationCode1
Open Problem: Active Representation Learning—0
Rotation Averaging: A Primal-Dual Method and Closed-Forms in Cycle Graphs—0
CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy—0
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