STEMbot climbs 7–33 mm stems with geometric PIN-SLAM, semantic OcTree mapping, and manifold-constrained A* planning, achieving sub-centimeter reconstructions and autonomous navigation on four plants.
Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
A transformer network estimates body-frame velocity and uncertainty from raw 4D radar spectral cubes, fused with IMU in a pose graph to achieve lower relative pose error than classical baselines on indoor sequences.
A real-time underwater SLAM system uses reliability-aware multi-sensor fusion and quadtree-guided 3D Gaussian mapping to maintain localization and photorealistic reconstruction during visual degradation.
Ground4D reconstructs dynamic 4D scenes from monocular video by initializing dynamic Gaussians from VGGT geometry and refining them with multi-view depth consistency at observed and virtual viewpoints.
citing papers explorer
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STEMbot: A Compliant Robot for Under-Canopy Plant Navigation
STEMbot climbs 7–33 mm stems with geometric PIN-SLAM, semantic OcTree mapping, and manifold-constrained A* planning, achieving sub-centimeter reconstructions and autonomous navigation on four plants.
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UNRIO: Uncertainty-Aware Velocity Learning for Radar-Inertial Odometry
A transformer network estimates body-frame velocity and uncertainty from raw 4D radar spectral cubes, fused with IMU in a pose graph to achieve lower relative pose error than classical baselines on indoor sequences.
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APVI-SLAM: Real-Time Acoustic-Pressure-Visual-Inertial Localization and Photorealistic Mapping System in Complex Underwater Environment
A real-time underwater SLAM system uses reliability-aware multi-sensor fusion and quadtree-guided 3D Gaussian mapping to maintain localization and photorealistic reconstruction during visual degradation.
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Ground4D: Consistency-Aware 4D Reconstruction from Monocular Video
Ground4D reconstructs dynamic 4D scenes from monocular video by initializing dynamic Gaussians from VGGT geometry and refining them with multi-view depth consistency at observed and virtual viewpoints.