Kimera-Semantics
Real-time 3D semantic reconstruction library for robotics, building dense metric-semantic maps from 2D sensor data.
What is Kimera-Semantics?
Kimera-Semantics is an open-source library that performs real-time 3D semantic reconstruction from 2D sensor data, such as RGB-D or stereo cameras. It builds dense, voxel-based maps where each element is labeled with a semantic class (like 'wall' or 'floor'), enabling robots to understand their environment beyond just geometry. It solves the problem of creating rich, semantically annotated 3D maps for autonomous navigation and scene understanding.
Robotics researchers and engineers working on autonomous systems, semantic SLAM, 3D scene understanding, and robotic perception who need real-time, dense semantic mapping capabilities.
Developers choose Kimera-Semantics for its real-time performance, tight integration with the ROS ecosystem, and lean design that builds on the robust Voxblox library. It provides a practical, open-source solution for adding semantic layers to geometric maps without the overhead of custom mesh management.
Overview
Real-Time 3D Semantic Reconstruction from 2D data
Use Cases
Best For
- Building real-time metric-semantic maps for autonomous drones or ground robots
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