gradslam/gradslam

Health v2 · maintenance only

Adoption not part of the score

1422 stars · 166 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

gradslam is a fully differentiable dense SLAM library built on PyTorch, providing differentiable building blocks such as nonlinear least squares solvers, ICP techniques, raycasting, and mapping/fusion modules. It enables end-to-end gradient flow from 3D maps and trajectories back to raw color/depth inputs for gradient-based learning in SLAM systems.

Use cases

• build a differentiable dense SLAM system in PyTorch
• backpropagate from 3D maps to 2D pixels for learned SLAM representations
• run differentiable ICP for point cloud registration
• fuse RGB-D frames into differentiable point cloud maps
• learn camera intrinsics or depth via gradient-based SLAM
• prototype neural SLAM research with differentiable raycasting

When to choose

• you need gradients to flow through SLAM components for deep learning research
• you work with RGB-D data and PyTorch
• you want differentiable ICP, raycasting, or TSDF-style fusion blocks

When to avoid

• you need a production real-time SLAM system without learning components
• you don't use PyTorch
• you need active maintenance or support for recent PyTorch versions

Facets

library · maturity maintenance

machine-learning simulation graphics computer-vision robotics deep-learning machine-learning simulation python cross-platform slam pytorch differentiable-programming 3d-reconstruction icp raycasting rgbd pointclouds gpu

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markdown · JSON · MCP: product_card(name="gradslam/gradslam")